Telematically monitoring and predicting a vehicle battery state

ABSTRACT

Apparatus, device, methods and system relating to a vehicular telemetry environment for monitoring vehicle components and providing indications towards the condition of the vehicle components and providing optimal indications towards replacement or maintenance of vehicle components before vehicle component failure.

This application claims the benefit under 35 U.S.C. § 119(e) to U.S.Provisional Application Ser. No. 62/627996, titled “TelematicsPredictive Vehicle Component Monitoring System”, filed on Feb. 8, 2018,which is incorporated herein in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to a system, method andapparatus for fleet management in vehicular telemetry environments. Morespecifically, the present disclosure relates to monitoring andpredicting component maintenance before an actual component failure tomaximize maintainability and operational status of a fleet of vehiclesthereby avoiding a vehicle breakdown.

BACKGROUND

Maintainability and identification of component failure is an importantaspect of fleet management. One past approach is to consider the MeanTime Between Failure engineering data to predict the elapsed timebetween inherent failures during normal operation of the vehicle.Another past approach is to apply the manufacturer's recommended vehiclemaintenance schedule. These past approaches are based upon a runningtotal of mileage or running total of operational time. Simplecomparisons of numbers are limited and inconclusive. Comparing a currentvalue with some previous value cannot accurately predict componentfailure.

One past application of telematics is U.S. Pat. No. 6,609,051 (Ser. No.09/948,938) issued to Feichter et al on Aug. 19, 2003 for a method andsystem for condition monitoring of vehicles. Another past application oftelematics is U.S. Pat. No. 8,244,779 (Ser. No. 13/253,599) issued toBorg & Copeland on Aug. 14, 2012 for a method and system for monitoringa mobile equipment fleet. Another past application of telematics is U.S.Pat. No. 9,734,528 (Ser. No. 14/203,619) issued to Gormley on Aug. 15,2017 for a vehicle customization and personalization activities. Anotherpast application of telematics is U.S. Pat. No. 9,747,626 (Ser. No.14/582,414) issued to Gormley on Aug. 29, 2017 for a vehiclecustomization and personalization activities.

SUMMARY

The present disclosure is directed to aspects in a vehicular telemetryenvironment. A new capability to process historical life cycle vehiclecomponent operational (usage) data and derive parameters to indicatevehicle component operational status may be provided. A new capabilityfor monitoring and predicting vehicle component replacement beforeactual component failure thereby maximizing maintainability andoperational status for each vehicle in a fleet of vehicles may also beprovided.

According to a first broad aspect, there is a method to identify realtime predictive indicators of operational vehicle component status isprovided. The method comprises accessing at least one record ofoperational component data, the operational component data includesoperational values from at least one vehicle component from at least onevehicle, the operational values representative of an operational lifecycle use of the at least one vehicle component, the operational valuesfurther based upon a measured component event, accessing at least onerecord of management event data the management event data, containing atleast one vehicle component event data point for at least one vehicle,associating the at least one record of operational component data withthe at least one record of management event data, filtering theoperational component data, deriving from the operational component dataat least one signal representative of the measured component event andcomparing filtered operational component data and at least one signalprior to the vehicle component event data point with filteredoperational component data and at least one signal post the vehiclecomponent event data point thereby identifying real time predictiveindicator of operational vehicle component status for real time use infleet management.

According to a another broad aspect, there is a system to identifyparameters indicative of a vehicle component status is provided. Thesystem comprises a telematics hardware device including a processor,memory, firmware and communication capability, a remote device includinga processor, memory, software and communications capability, thetelematics hardware device monitoring at least one vehicle componentfrom at least one vehicle and logging operational component data of theat least one vehicle component, the telematics hardware devicecommunicating a log of operational component data to the remote device,the remote device accessing at least one record of operational componentdata, the operational component data including operational values fromat least one vehicle component from at least one vehicle, theoperational values representative of an operational life cycle use ofthe at least one vehicle component, the operational values further basedupon a measured component event, the remote device further accessing atleast one record of management event data, the management event datacontaining at least one vehicle component event data point for at leastone vehicle, the remote device associating the at least one record ofoperational component data with the at least one record of managementevent data, the remote device filtering the operational component dataand deriving from the operational component data at least one signalrepresentative of the measured component event, the remote devicecomparing filtered operational component data and at least one signalprior to the vehicle component event data point with filteredoperational component data and at least one signal post the vehiclecomponent event data point thereby identifying real time predictiveindicators of operational vehicle component status for real time use infleet management.

In an embodiment, the operational vehicle component status includes lifecycle status representative of at least one or more of a new componentstate, a good component state, a fair component state, a poor componentstate, a replace component state or a failure component state.

In an embodiment, the operational component data may include datarepresentative of at least one category of fuel and/or air metering,emission control, ignition system control, vehicle idle speed control,transmission control, hybrid propulsion or battery information. Inanother embodiment, the operational component data may include databased upon at least one of on-board diagnostic fault codes, troublecodes, manufacturer codes, generic codes or vehicle specific codes.

In an embodiment, the operational values from at least one vehiclecomponent may include one or more values representative of thermostat,or temperature sensors, oil sensors, fuel sensors, coolant sensors,transmission fluid sensors, electric motor coolant sensors battery,pressure sensors oil pressure sensors, fuel pressure sensors, crankcasesensors, hydraulic sensors, fuel volume, fuel shut off, camshaftposition sensors, crankshaft position sensors, O2 sensors turbochargersensors, waste gate sensors, air injection sensors, mass air flowsensors, throttle body sensors, air metering sensors, emission sensorsthrottle position sensors, fuel delivery, fuel timing, system lean,system rich, injectors, cylinder timing, engine speed conditions, chargeair cooler bypass, fuel pump sensors, intake air flow control, misfireindications, accelerometer sensors, knock sensors, glow plug sensors,exhaust gas recirculation sensors, air injection sensors, catalyticconvertor sensors, evaporative emission sensors, brake sensors, idlespeed control sensors, throttle position, air conditioning sensors,power steering sensors, system voltages, engine control module values,starter motor voltage, starter motor current, torque converter sensors,fluid sensors, output shaft speed values, gear position, transfer box,converter status interlock torque values, hybrid batter pack values,cooling fan values and inverter and battery voltages.

In an embodiment, the operational life cycle may include operationalvalues from a new component to a failed component.

In an embodiment, the measured component event is an event that providesa higher operational load within the limits of at least one vehiclecomponent. In another embodiment, the measured component event is acranking event for at least one vehicle. In another embodiment, thecranking event is detected by sensing a voltage decrease over timefollowed by an indication of engine RPM. In another embodiment, thecranking event is detected by sensing a voltage decrease over timefollowed by an indication of vehicle speed. In another embodiment, adetected cranking event creates at least one record of operationalcomponent data in the form of a series of battery voltages. In anotherembodiment, the series of battery voltages may include one or morevalues indicative of ignition on, starter motor cranking, batterycharging and battery recovery.

In an embodiment, filtering determines a moving average of theoperational values. In another embodiment, filtering determines arunning average of the operational values.

In an embodiment, the at least one signal is determined when a singledata point from the operational values is above an upper control limitor below a lower control limit. In another embodiment, the at least onesignal is determined when a series of eight consecutive data points fromthe operational values are between a mean value and an upper controllimit or between a mean value and a lower control limit. In anotherembodiment, the at least one signal is determined when a series of fourout of five consecutive data points from the operational values isbetween a mean value and greater than plus one standard deviation orbetween the mean value and greater than minus one standard deviation. Inanother embodiment, the at least one signal is determined when a seriesof two out of three consecutive data points from the operational valuesis between a mean value and greater than plus two standard deviation orthe mean value and greater than minus two standard deviation. In anotherembodiment, the cranking event produces lower voltages therebyincreasing a number of signals from the measured component event.

In an embodiment, a monitoring indicator framework may be includedproviding a gauge for identifying the real time predictive indicators ofoperational vehicle component status. In another embodiment, themonitoring indicator framework may include one or more of a lowercontrol limit, an upper control limit, plus one standard deviation, plustwo standard deviation, a mean value, minus one standard deviation andminus two standard deviation derived from the operational componentdata. In another embodiment, the monitoring indicator framework mayinclude a first zone, the first zone representative of a new componentstate where the at least one signal includes a mix of “B” signal valuesand “Y” signal values, the “B” signal values and the “Y” signal valuesare disposed between a mean value and an upper control limit value. Inanother embodiment, the monitoring indicator framework may include asecond zone, the second zone representative of a good component statewherein the at least one signal includes a mix of “Y” signal values and“O” signal values with a smaller number of “B” signal values, and the“Y” signal values, the “O” signal values and the “B” signal values arepredominately disposed between a mean value and an upper control limitvalue. In another embodiment, the monitoring indicator frameworkincludes a third zone, the third zone representative of a fair componentstate wherein the at least one signal includes a mix of “Y” signalvalues, “O” signal values, “B” signal values and “R” signal values, the“Y” signal values and “O” signals values are disposed above and below amean value, the “B” signal values are below the mean value and above alower control limit, and the “R” signal values are below the lowercontrol limit. In another embodiment, the monitoring indicator frameworkincludes a fourth zone, the fourth zone representative of a poorcomponent state wherein the at least one signal includes a mix of “Y”signal values, “O” signal values and “B” signal values and the “Y”signal values, “O” signal values and the “B” signal values are disposedbetween a mean value and a lower control limit. In another embodiment,the monitoring indicator framework includes a fifth zone, the fifth zonerepresentative of a failed component state wherein the at least onesignal include “R” signal values and a grouping of “R” signal valuesdisposed below a lower control limit.

In an embodiment, a plurality of signals is above a moving average ofthe operation component data. In another embodiment, the plurality ofsignals is on either side of a moving average of the operationalcomponent data. In another embodiment, the plurality of signals is belowa moving average of the operational component data. In anotherembodiment, the plurality of signals is initially above a moving averageof the operation component data and then above and below a movingaverage of the operational component data.

In an embodiment, a moving average of the operational component data isbetween an upper control limit value and a mean value. In anotherembodiment, a moving average of the operational component data isbetween plus one standard deviation and minus one standard deviation. Inanother embodiment a moving average of the operational component data isbetween the mean value and minus two standard deviation. In anotherembodiment, a moving average of the operational component data isbetween minus two standard deviation and plus two standard deviation. Inanother embodiment, a moving average of the operational component datais between the mean value and plus two standard deviation.

In an embodiment, the operational vehicle component status is replacevehicle component when the real time predictive indicators are a movingaverage of the operational component data decreasing from a mean valueto minus one standard deviation. In another embodiment, the operationalvehicle component status is replace vehicle component when the real timepredictive indicators are a moving average of the operational componentdata is decreasing from minus one standard deviation to minus twostandard deviation. In another embodiment, the operational vehiclecomponent status is replace vehicle component when the real timepredictive indicators are a moving average that reaches minus twostandard deviation.

In an embodiment, the real time predictive indicators further include atleast one “R” signal value. In another embodiment, the at least one “R”signal value is at a lower control limit. In another embodiment, the atleast one “R” signal is below a lower control limit.

In an embodiment, the operational vehicle component status is a newvehicle component when the real time predictive indicators are a movingaverage between plus one standard deviation and plus two standarddeviation. In another embodiment the operational vehicle componentstatus is a good vehicle component when the real time predictiveindicators are a moving average between plus one standard deviation andminus two standard deviation. In another embodiment, the operationalvehicle component status is a fair vehicle component when the real timepredictive indicators are a moving average at or below minus twostandard deviation. In another embodiment, the operational vehiclecomponent status is a poor vehicle component when the real timepredictive indicators are at least one signal at or below a lowercontrol limit. In another embodiment, the operational vehicle componentstatus is a replace vehicle component when the real time predictiveindicators are a moving average rising from minus one standard deviationto above plus one standard deviation followed by a moving averagebetween plus one standard deviation and plus two standard deviation. Inanother embodiment, the operational vehicle component status is replacevehicle component when the real time predictive indicators are a movingaverage decreasing from minus one standard deviation towards minus twostandard deviation. In another embodiment, the operational vehiclecomponent status is new and the real time predictive indicators are aplurality of signals above and below a moving average the plurality ofsignals are further above a mean value and below an upper control limit.In another embodiment, the plurality of signals include “B” signalvalues, “O” signal values and “Y” signal values. In another embodiment,the operational vehicle component status is good and the real timepredictive indicators are a plurality of signals above a moving averageand below plus one standard deviation and the plurality of signals arepredominately “O” signal values and “Y” signal values. In anotherembodiment, the operational vehicle component status is fair and thereal time predictive indicators are a plurality of signals on eitherside of a decreasing moving average. In another embodiment, theplurality of signals include “Y” signal values and “B” signal values. Inanother embodiment, the plurality of signals are between a mean valueand a lower control limit. In another embodiment, the operationalvehicle component status is poor and the real time predictive indicatorsare a plurality of signals below a lower control limit. In anotherembodiment, the plurality of signals are “R” signal values.

According to another broad aspect, there is a method to monitor acondition of an operational vehicle component is provided. The methodcomprises: accessing a sample of operational component data thatincludes operational data from at least one vehicle, determiningoperational component parameters from the operational data, theoperational component parameters indicative of a current operation ofthe at least one vehicle component, comparing the sample of operationalcomponent parameters with predictive indicator parameters, thepredictive indicator parameters predetermined from historicaloperational life cycle use of at least one other vehicle component, andindicating a condition of the operational vehicle component based upon acomparison of the operational component parameters and the predictiveindicator parameters.

According to another broad aspect, there is a system to monitor acondition of an operational vehicle component is provided. The systemcomprises: a telematics hardware device including a processor, memory,firmware and communications capability, a remote device including aprocessor memory software and communications capability, the telematicshardware device monitoring at least one vehicle component from at leastone vehicle and logging operational component data of the at least onevehicle component, the telematics hardware device communicating a log ofoperational component data to the remote device, the remote deviceaccessing a sample of operational component data that includesoperational data from at least one vehicle component from at least onevehicle, the remote device determining operational component parametersfrom the operational data, the operational component parametersindicative of a current operation of the at least one vehicle component,the remote device comparing the sample of operational componentparameters with predictive indicator parameters, the predictiveindicator parameters predetermined from historical operational lifecycle use of at least one other vehicle component, and the remote deviceindicating a condition of the operational vehicle component based upon acomparison of the operational component parameters and the predictiveindicator parameters.

In an embodiment, the operational data is a series of at least eightconsecutive operational data points from the at least one vehiclecomponent.

In another embodiment, the operational component parameters include amoving average representative of the current operation.

In another embodiment, the operational component parameters includesignals representative of the current operation of a vehicle component.The signals may include one or more of “R” signal values, “Y” signalvalues, “O” signal values and/or “B” signal values.

In another embodiment, the at least one vehicle component includes oneor more values representative of thermostat or temperature sensors, oilsensors, fuel sensors, coolant sensors, transmission fluid sensors,electric motor coolant sensors, battery, pressure sensors, oil pressuresensors, fuel pressure sensors, crankcase sensors, hydraulic sensors,fuel volume, fuel shut off, camshaft position sensors, crankshaftposition sensors, O2 sensors, turbocharger sensors, waste gate sensors,air injection sensors, mass air flow sensors, throttle body sensors, airmetering sensors, emission sensors, throttle position sensors, fueldelivery, fuel timing, system lean, system rich, injectors, cylindertiming, engine speed conditions, charge air cooler bypass, fuel pumpsensors, intake air flow control, misfire indications, accelerometersensors, knock sensors, glow plug sensors, exhaust gas recirculationsensors, air injection sensors, catalytic convertor sensors, evaporativeemission sensors, brake sensors, idle speed control sensors, throttleposition, air conditioning sensors, power steering sensors, systemvoltages, engine control module values, starter motor voltage, startermotor current, torque converter sensors, fluid sensors, output shaftspeed values, gear position, transfer box, converter status, interlock,torque values, hybrid battery pack values, cooling fan values andinverter and battery voltages.

In another embodiment, the predictive indicator parameters include amoving average representative of the historical operational life cycleuse of a vehicle component. The predictive indicator parameters may alsoinclude a mean representative of the historical operational life cycleuse of the vehicle component. The predictive indicator may also includea lower control limit representative of the historical operational lifecycle use of the vehicle component. The predictive indicator may alsoinclude an upper control limit representative of the historicaloperational life cycle use of the vehicle component. The predictiveindicator may also include a standard deviation representative of thehistorical operational life cycle use of the vehicle component. In someembodiments, the standard deviation may include plus one standarddeviation, plus two standard deviation, minus one standard deviation andminus two standard deviation. In some embodiments, the life cycle use ofthe vehicle component is from a new component to a failed component. Acondition may be one of a new component, a good component, a faircomponent or a poor component.

In another embodiment, the operational component parameters include amoving average of the operational component data, the predictiveindicator parameters include minus one standard deviation and a minustwo standard deviation of the operational life cycle data and the movingaverage is increasing in value between the minus one standard deviationand the minus two standard deviation thereby indicating a new componentfor the condition of the operational vehicle component.

In another embodiment, the operational component parameters include amoving average of the operational component data, the predictiveindicator parameters include a minus one standard deviation and a meanof the operational life cycle data, and the moving average is increasingin value between the minus one standard deviation and the mean therebyindicating a new component for the condition of the operational vehiclecomponent.

In another embodiment, the operational component parameters include amoving average of the operational component data, the predictiveindicator parameters include a mean and plus one standard deviation ofthe operational life cycle data and the moving average is increasing invalue between the mean the plus one standard deviation therebyindicating a new component for the condition of the operational vehiclecomponent.

In an embodiment, the operational component parameters further includesignals of the operational component data, the predictive indicatorparameters include a mean and an upper control limit of the operationallife cycle data and the signals are between the mean and the uppercontrol limit. In another embodiment, the operational componentparameters further include signals of the operational component data,the predictive indicator parameters include an upper control limit ofthe operational life cycle data and the signals are between the mean andthe upper control limit. In another embodiment, the operationalcomponent parameters further include signals of the operationalcomponent data, the predictive indicator parameters include an uppercontrol limit of the operational life cycle data and the signals arebetween the mean and the upper control limit. In another embodiment, theoperational component parameters include signals of the operationalcomponent data, the predictive indicator parameters include a mean andan upper control limit of the operational life cycle data and thesignals are between the plus two standard deviation and the uppercontrol limit thereby indicating anew component for the condition of theoperational vehicle component. In some embodiments, the signals include“B” signal values, “Y” signal values and “O” signal values.

In another embodiment, the operational component parameters include amoving average of the operational component data, the predictiveindicator parameters include a mean and plus two standard deviation ofthe operational life cycle data and the moving average is between themean and the plus two standard deviation of the operational life cycledata and the moving average has a relatively constant slope therebyindicating a good component for the condition of the operational vehiclecomponent. In an embodiment, the operational component parametersinclude signals of the operational component data and the signals arebetween the mean and the plus two standard deviation of the operationallife cycle data. The signals may include “Y” signal values and “O”signal values. Alternatively, the signals may further include “B” signalvalues.

In another embodiment, the operational component parameters include amoving average of the operational component data, the predictiveindicator parameters include a mean and minus one standard deviation ofthe operational life cycle data, and the moving average is decreasing invalue between the mean and the minus one standard deviation therebyindicating a fair component for the condition of the operational vehiclecomponent. In an embodiment, the operational component parametersinclude signals of the operational component data, the predictiveindicator parameters further include a lower control limit and thesignals are between the mean and the loser control limit of theoperational life cycle data. The signals may include “O” signal values,“Y” signal values and “B” signal values. The signals may also furtherinclude “R” signal values below the lower control limit.

In another embodiment, the operational component parameters include amoving average of the operational component data, the predictiveindicator parameters include minus one standard deviation and minus twostandard deviation of the operational life cycle data and the movingaverage is decreasing in value between the minus one standard deviationand minus two standard deviation thereby indicating a fair component forthe condition of the operational vehicle component. In an embodiment,the operational component parameters include signals of the operationalcomponent data and the predictive indicator parameters further include amean and a lower control limit and the signals are between the mean andthe lower control limit of the operational life cycle data. The signalsmay include “O” signal values, “Y” signal values and “B” signal values.The signals may further include “R” signal values below a lower controllimit.

In another embodiment, the operational component parameters include amoving average and signals of operational component data, the predictiveindicator parameters include a mean, minus one standard deviation and alower control limit and the moving average is decreasing between themean and the one standard deviation and the signals are below the lowercontrol limit thereby indicating a poor component for the condition ofthe operational vehicle component. In and embodiment, the signals are“R” signal values.

In another embodiment, the operational component parameters include amoving average and signals of operational component data, the predictiveindicator parameters include a minus one standard deviation, minus twostandard deviation and a lower control limit and the moving average isdecreasing between the one standard deviation and the two standarddeviation and the signals are below the lower control thereby indicatinga poor component for the condition of the operational vehicle component.In and embodiment, the signals are “R” signal values.

In another broad aspect, there is a method to monitor and providereplacement indications for an operational vehicle component is providedand comprises accessing a sample of operational component data thatincludes an operational data from at least one vehicle component from atleast one vehicle, determining operational component parameters from theoperational data, the operational component parameters indicative of acurrent operation of the at least one vehicle component, comparing thesample of operational component parameters with predictive indicatorparameters, the predictive indicator parameters predetermined fromhistorical operational life cycle use of at least one other vehiclecomponent, and indicating a maintenance condition of the operationalvehicle component based upon a comparison of said operational componentparameters and the predictive indicator parameters.

In another broad aspect, there is a system to monitor and providereplacement indications for an operational vehicle component is providedand comprises a telematics hardware device including a processor,memory, firmware and communications capability, a remote deviceincluding a processor memory software and communications capability, thetelematics hardware device monitoring at least one vehicle componentfrom at least one vehicle and logging operational component data of theat least one vehicle component, the telematics hardware devicecommunicating a log of operational component data to the remote device,the remote device accessing a sample of operational component data thatinclude an operational data from at least one vehicle component from atleast one vehicle, the remote device determining operational componentparameters from the operational data, the operational componentparameters indicative of a current operation of the at least one vehiclecomponent, the remote device comparing the sample of operationalcomponent parameters with predictive indicator parameters, thepredictive indicator parameters predetermined from historicaloperational life cycle use of at least one other vehicle component, andthe remote device indicating a maintenance condition of the operationalvehicle component based upon a comparison for the operational componentparameters and the predictive indicator parameters.

In an embodiment, the operational component parameters include a movingaverage and signals of the operational component data, the predictiveindicator parameters include a mean, minus one standard deviation and alower control limit, the moving average decreasing from the mean to theminus one standard deviation, the signals including first signals, thefirst signals between the mean and the lower control limit, the signalincluding second signals, the second signals below the lower controllimit thereby indicating a maintenance condition of replacement warning.The first signals may be above and below a moving average. The firstsignals may include “O” signal values, “B” signal values and “Y” signalvalues. The second signals may include “R” signal values.

In an embodiment, the operational component parameters include a movingaverage and signals of the operational component data, the predictiveindicator parameters include a mean, minus one standard deviation, minustwo standard deviation and a lower control limit, the moving averagedecreasing from the minus one standard deviation to the minus twostandard deviation, the signals including first signals, the firstsignals between the mean and the lower control limit, the signalincluding second signals, the second signals below the lower controllimit thereby indicating a maintenance condition of replacement. Thefirst signals may be above and below the moving average. The firstsignals may include “O” signal values, “B” signal values and “Y” signalvalues. The second signals may include “R” signal values.

According to another broad aspect, there is a method to assesshistorical vehicle component maintenance and identify predictiveindicators or maintenance events is provided and comprises: accessing arecord of operational component data, the operational component dataincluding operational values from a vehicle component from a vehicle,the operational values representative of an operational life cycle useof the vehicle component, the operational values further based upon ameasured component event, accessing a record of management event data,the management event data containing a vehicle component event datapoint for the vehicle, the vehicle component event data point includinga date and a maintenance event indication, associating the record ofoperational component data with the record of management event data,filtering the operational component data, the filtering including amoving average of the operational component data, an upper control limitof the operational component data, plus two standard deviation of theoperational component data, plus one standard deviation of theoperational component data, a mean of the operational component data,minus one standard deviation of the operational component data, minustwo standard deviation of the operational component data and a lowercontrol limit of the operational component data, deriving from theoperational component data at least one signal representative ofoperational use of the vehicle component for the measured componentevent, comparing filtered operational component data with the at leastone signal prior to the vehicle component event data point therebyidentifying indicators associated with the maintenance event.

According to another broad aspect, there is a system to assesshistorical vehicle component maintenance and identify predictiveindicators or maintenance events is provided and comprises: a telematicshardware device including a processor, memory, firmware andcommunications capability, a remote device including a processor memorysoftware and communications capability, the telematics hardware devicemonitoring at least one vehicle component from at least one vehicle andlogging operational component data of the at least one vehiclecomponent, the telematics hardware device communicating a log ofoperational component data to the remote device, the remote deviceaccessing a record of operational component data, the operationalcomponent data including operational values from a vehicle componentfrom a vehicle, the operational values representative of an operationallife cycle use of the vehicle component, the operational values furtherbased upon a measured component event, the remote device accessing arecord of management event data, the management event data containing avehicle component event data point for the vehicle, the vehiclecomponent event data point including a date and a maintenance eventindication, the remote device associating the record of operationalcomponent data with the record of management event data, the remotedevice filtering the operational component data, the filtering includinga moving average of the operational component data, an upper controllimit of the operational component data, plus two standard deviation ofthe operational component data, plus one standard deviation of theoperational component data, a mean of the operational component data,minus one standard deviation of the operational component data, minustwo standard deviation of the operational component data and a lowercontrol limit of the operational component data, the remote devicederiving from the operational component data at least one signalrepresentative of operational use of the vehicle component for themeasured component event, the remote device comparing filteredoperational component data with the at least one signal prior to thevehicle component event data point thereby identifying indicatorsassociated with the maintenance event indication.

In an embodiment, the maintenance event indication is a componentfailure and the comparing identifies failure indicators, the failureindicators include at least one of the following indicators: a firstfailure indicator, the first failure indicator having a decreasingmoving average from the mean of the operational component data to theminus one standard deviation of the operational component data, a secondfailure indicator, the second failure indicator having first signalsabove and below the decreasing moving average, a third failureindicator, the third failure indicator having second signals below thelower control limit of the operational component data. In one embodimentthe first signals include “O” signal values, in another embodiment thefirst signals include “Y” signal values and in another embodiment thefirst signals include “B” signal values. In another embodiment thesecond signals include “R” signal values. In another embodiment thesecond signals are a plurality of second signals decreasing in valueaway from the lower control limit of the operational component data.

In another embodiment, the comparing filtered operational component datawith the at least one signal includes post the vehicle component eventdata point and the maintenance event indication is a componentreplacement and the component replacement was premature, the comparingidentifies premature component replacement indicators, the prematurecomponent replacement indicators include at least one of the followingindicators: a first indicator, the first indicator having a movingaverage relatively constant between the mean and the plus one standarddeviation prior to the vehicle component data point, the first indicatorhaving a moving average increasing in value from the mean to the plusone standard deviation followed by a relatively constant moving averageat the plus one standard deviation, a second indicator, the secondindicator having signals above the lower control limit, and a thirdindicator the third indicator having signals above the mean.

In an embodiment, the comparing filtered operational component data withthe at least one signal includes post the vehicle component event datapoint and the maintenance event indication is component maintenance andthe comparing identifies component maintenance indicators, the componentmaintenance indicators include at least one of the following indicators:a first indicator, the first indicator having a moving averagerelatively constant and above minus two standard deviation prior to thevehicle component data point and a moving average increasing in valuepost the vehicle component data point and a second indicator, the secondindicator having signals above the lower control limit.

In an embodiment, the comparing filtered operational component data withthe maintenance event indication is component failure and the comparingidentifies indicators for an incorrect data for the maintenance eventindication, the indicators include at least one of the followingindicators: a first failure indicator, the first failure indicator wherethe moving average is not decreasing from the mean of the operationalcomponent data to the minus one standard deviation of the operationalcomponent data, a second failure indicator, the second failure indicatorwhere the first signals are not above and below a decreasing movingaverage, and a third failure indicator, the third failure indicatorwhere second signals are not below the lower control limit of theoperational component data.

In another broad aspect, there is a method of monitoring a battery of avehicle. The method comprises receiving a plurality of voltage signalsindicating a change in voltage of the battery at times associated with aplurality of crankings of a starter motor of the vehicle; determiningfor each of the plurality of voltage signals, a maximum voltage and aminimum voltage of the voltage signal, to generate a plurality ofmaximum voltages and a plurality of minimum voltages for a time period,determining a variance in the plurality of minimum voltages for the timeperiod, and generating a prediction of a state of the battery based atleast in part on the plurality of maximum voltages, the plurality ofminimum voltages and the variance of the plurality of minimum voltages.

In another broad aspect, there is a system of monitoring a battery of avehicle. The system comprises a telematics hardware device including aprocessor, memory, firmware and communications capability, a remotedevice including a processor, memory, software and communicationscapability, the telematics hardware device monitoring a battery of avehicle and logging a plurality of voltage signals, the telematicshardware device communicating a log including a plurality of voltagesignals, the remote device receiving a plurality of voltage signalsindicating a change in voltage of the battery at times associated with aplurality of crankings of a starter motor of the vehicle; the remotedevice determining, for each of the plurality of voltage signals, amaximum voltage and a minimum voltage of the voltage signal, to generatea plurality of maximum voltages and a plurality of minimum voltages fora time period; the remote device determining a variance in the pluralityof minimum voltages for the time period; the remote device generating aprediction of a state of the battery based at least in part on theplurality of maximum voltages, the plurality of minimum voltages, andthe variance of the plurality of minimum voltages.

In an embodiment, wherein receiving the plurality of voltage signalscomprises receiving the plurality of voltage signals via a vehicletelematics system of the vehicle. In another embodiment, whereingenerating the prediction of the state of the battery comprisesgenerating a prediction of whether and/or when the battery is likely tofail. IN another embodiment, wherein generating the prediction of thestate of the battery comprises generating a prediction of whether and/orwhen the battery is likely to fail based at least in part on anenvironmental condition. In another embodiment, wherein theenvironmental condition is temperature.

These and other aspects and features of non-limiting embodiments areapparent to those skilled in the art upon review of the followingdetailed description of the non-limiting embodiments and theaccompanying drawings. Further, it should be appreciated that theforegoing concepts, and additional concepts discussed below, may bearranged in any suitable combination, as the present disclosure is notlimited in this respect.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary non-limiting embodiments are described with reference to theaccompanying drawings in which:

FIG. 1 is a high level diagrammatic view of a vehicular telemetry dataenvironment and infrastructure;

FIG. 2a is a diagrammatic view of a vehicular telemetry hardware systemincluding an on-board portion and a resident vehicular portion;

FIG. 2b is a diagrammatic view of a vehicular telemetry hardware systemcommunicating with at least one intelligent I/O expander;

FIG. 2c is a diagrammatic view of a vehicular telemetry hardware systemwith an integral wireless communication module capable of communicationwith at least one beacon module;

FIG. 2d is a diagrammatic view of at least on intelligent I/O expanderwith an integral wireless communication module capable of communicationwith at least one beacon module;

FIG. 2e is a diagrammatic view of an intelligent I/O expander and devicecapable of communication with at least one beacon module;

FIG. 3 is a diagrammatic view of raw vehicle component data over aperiod of time illustrating raw data representative of the vehiclecomponent useful life correlated with an event such as vehicle componentfailure or vehicle component maintenance;

FIG. 4 is a diagrammatic view of a series of normal distributions of rawvehicle component data varying over the useful life of the vehiclecomponent illustrating the distributions of a new vehicle component, adepreciating vehicle component and a failed vehicle component;

FIG. 5 is a diagrammatic view of a normal distribution for a new vehiclecomponent compared to a normal distribution for a failed vehiclecomponent;

FIG. 6 is a diagrammatic view illustrating the degradation of vehiclecomponent parameters and increasing variances over the life cycle of avehicle component from a new vehicle component to a failed vehiclecomponent;

FIG. 7 is a diagrammatic view illustrating a framework that monitorsvehicle component process change over time for identifying the state ofthe vehicle component;

FIG. 8 is a diagrammatic view illustrating a moving average of rawvehicle component data over the life cycle of a vehicle component withan event such as a failed vehicle component or maintenance of a vehiclecomponent;

FIG. 9 is a diagrammatic view illustrating a moving average of vehiclecomponent data over the life cycle of a vehicle component with an eventsuch as a failed vehicle component or maintenance of a vehicle componentsuperimposed with a monitoring framework;

FIG. 10 is a diagrammatic view illustrating a moving average of vehiclecomponent data over the life cycle of a vehicle component with an eventsuch as a failed vehicle component or maintenance of a vehicle componentincluding a set of signal monitoring indicators;

FIG. 11 is a diagrammatic view illustrating a moving average of vehiclecomponent data over the life cycle of a vehicle component with an eventsuch as a failed vehicle component or maintenance of a vehicle componentincluding a set of category monitoring indicators;

FIG. 12 is a diagrammatic view illustrating a moving average of vehiclecomponent data over the life cycle of a vehicle component with an eventsuch as a failed vehicle component or maintenance of a vehicle componentincluding both a set of signal monitoring indicators and a set ofcategory monitoring indicators;

FIG. 13 is a diagrammatic view illustrating a moving average of vehiclecomponent data over the life cycle of a vehicle component with an eventsuch as a failed vehicle component or maintenance of a vehicle componentincluding a set of signal monitoring indicators and an alternative setof category monitoring indicators;

FIG. 14 is a diagrammatic view of raw vehicle component data, movingaverage vehicle component data, a plurality of signals, an event and avehicle component monitoring framework;

FIG. 15 is a diagrammatic view of raw vehicle component data, movingaverage vehicle component data, a plurality of signals, an event and avehicle monitoring framework based upon grouping of like signals;

FIG. 16 is an alternative view of FIG. 15 illustrating the vehiclemonitoring framework for a vehicle component from good condition, poorcondition, failed condition and replacement condition;

FIG. 17 is an enlarged view of a section of FIG. 15 surrounding thevehicle component event illustrating the vehicle component data, movingaverage of vehicle component data and a plurality of different signals;

FIG. 18 is a diagrammatic view illustrating a moving average of vehiclecomponent data and signals before and after a vehicle component event;

FIG. 19 is a diagrammatic view illustrating a moving average of vehiclecomponent data and signals near a vehicle component event representativeof a component failure event;

FIG. 20 is a diagrammatic view illustrating a moving average of vehiclecomponent data and signals near a vehicle component event representativeof an unrelated event;

FIG. 21 is a diagrammatic view illustrating a moving average of vehiclecomponent data and signals near a vehicle component event representativeof a premature component replacement;

FIG. 22 is a diagrammatic view illustrating a moving average of vehiclecomponent data and signals near a vehicle component event representativeof an incorrect event data for a vehicle component failure;

FIG. 23 is a diagrammatic view illustrating a moving average of vehiclecomponent data and a plurality of signals representative of vehiclecomponent status for a corrective action;

FIG. 24 is a diagrammatic view illustrating a moving average of vehiclecomponent data and a plurality of signals representative of vehiclecomponent status over the life cycle of the vehicle component with acomponent monitoring indicator in the form of a component rating;

FIG. 25 is a diagrammatic view illustrating different sources of rawdata for vehicle component failure analysis and prediction;

FIG. 26 is a diagrammatic view of a process for predictive componentfailure analysis;

FIG. 27 is a diagrammatic view of a process for detecting componentfailure in association with FIG. 25;

FIG. 28 is a diagrammatic view of a process for detecting prematurecomponent replacement in association with FIG. 25;

FIG. 29 is a diagrammatic view of a process for detecting inactivecomponent maintenance in association with FIG. 25;

FIG. 30 is a diagrammatic view of a process for detecting an erroneousevent date in association with FIG. 25;

FIG. 31 is a diagrammatic view of a process for monitoring and reportinga vehicle component condition;

FIG. 32 is a diagrammatic view of a process for monitoring and reportinga vehicle component replacement condition;

FIG. 33 is a diagrammatic view of a voltage curve from a good batterybased upon a vehicle cranking event illustrating the battery voltagedrop, dwell time and recovery slope to recharge the battery;

FIG. 34 is a diagrammatic view of a voltage curve from a poor batterybased upon a vehicle cranking event illustrating the battery voltagedrop, dwell time and recovery slop to recharge the battery;

FIG. 35 is a diagrammatic view of a process for determining predictiveindicators of vehicle component status; and

FIG. 36 is a diagrammatic view of a process for identifying a vehiclecomponent status and the associated predictive indicators.

The drawings are not necessarily to scale and are diagrammaticrepresentations of the exemplary non-limiting embodiments of the presentinvention.

DETAILED DESCRIPTION

Described herein are techniques for monitoring operational components ofvehicle, including electrical components and other components of avehicle, to generate information on a state of an operational componentover time and to generate a prediction of whether and/or when anoperational component is likely to fail. In some embodiments, for eachoperational component that is monitored in this manner, one or moresignals, generated by the operational component during an event thatcorresponds to a particular operation of the operational component, aremonitored and characteristic values of the operational parameter(s)generated by the component during the event are determined (e.g.,through statistical analysis of the signals) and used in generating theprediction of whether and/or when the operational component is likely tofail. The prediction generated in this manner may be reliably used todetermine whether and when to perform maintenance on a vehicle, torepair or replace the operational component before failure.

Such techniques for generating predictions of whether and/or when anoperational component is likely to fail may be advantageous in someenvironments. Conventionally, there was no reliable way to predict whenan operational component would fail. Manufacturers often publishinformation on their products, including “mean time between failure”(MTBF) information, that may indicate when the manufacturer expects afailure might occur. Unfortunately, this product information is whollyunreliable. Manufacturers tend to be very cautious in setting theseproduct life estimates. This not only mitigates the risk of a productunexpectedly failing earlier than predicted, which may lead to a productowner suffering inconvenience from a product failure, but alsoencourages purchase of replacement products early, which may benefit themanufacturer as over time more products are purchased than otherwisewould be. However, while early replacement benefits the manufacturer,early replacement is an unnecessary expense to a product owner. When aproduct owner owns hundreds or thousands of vehicles, over time, earlyand unnecessary replacement of parts can add up to a substantial cost,potentially millions of dollars, as compared to timely replacement.

Additionally, past approaches generated such product lifespan estimatesusing assumptions related to normal operation of a vehicle based upon apre-established set of operating conditions, which may includeoperational criteria for a vehicle. In reality, vehicles are typicallyoperated outside of such pre-established operating conditions such as,for example, a range of altitudes from sea-level to several thousandfeet above sea-level, extreme cold temperatures, extreme hottemperatures, on highly rough roads causing significant vibration, andin mountainous terrains or flat terrains as well as other operationalcriteria. Vehicles may also be operated through four seasons that createfour distinct operational environments. Operating a vehicle outside ofnormal operating conditions impacts the frequency and time betweenfailure. Of course, few vehicles may have been operated perfectly withinthe assumptions that underlay the product lifespan estimates,undermining the reliability of the estimates for (or even making theestimates useless for, in some cases) real-world purposes.

Given the unreliability of manufacturer estimates, owners of such fleetsof vehicles have therefore, conventionally, attempted to generate theirown approximate predictions of failures of operational components, basedprimarily on time since an operational component was installed. Fleetowners are well aware, however, that this is also notoriouslyunreliable. Particularly when a fleet is used over a wide geographicarea (e.g., a whole country), different vehicles in a fleet mayencounter vastly different operating conditions, such as differentenvironmental factors, road conditions, different operating styles thatmay yield different characteristics of vehicle operation (e.g., greateracceleration, greater speed, harder braking, etc.), different distancestraveled, different loads carried, or other factors that influenceoperation of the vehicle. When there is significant variation inoperating conditions, there may be significant variation in life span ofoperational components of a vehicle, including the operating conditionsdiscussed in the preceding paragraph. Accordingly, while fleet ownersmay create a maintenance schedule for their vehicles to repair orreplace operational components, such a schedule may not reliably predictfailures in individual vehicles. Vehicles may therefore experiencefailures prior to a planned maintenance, which can significantlyincrease costs for fleet owners that may need to tow a vehicle to berepaired, repair the vehicle, make arrangements for transporting peopleand/or cargo that had been being transported by the failed vehicle, andaccommodate schedule delays from the change in transportation of thepeople/cargo. These may be significant costs. As a result, as withmanufacturer estimates, some fleet owners may replace operationalcomponents earlier than may be needed, which has its own substantialcosts, as discussed above.

This lack of reliable prediction systems for failure or deterioration ofvehicle operational components has presented difficulties to vehiclefleet operators for decades, and costs such fleet owners millions ofdollars. The inventors have recognized and appreciated that there wouldbe significant advantages for fleet owners if a reliable form ofprediction could be offered.

The inventors recognized and appreciated the advantages that would beoffered by a reliable prediction system that would monitor a vehicle andoperational components of a vehicle in real time, during use of thevehicle, to generate a prediction specific to that vehicle and specificto that time. Such a system that generates a prediction unique to eachvehicle would have advantages over systems that generate information onaverage lifespans of products, given the significant inter-vehiclevariation mentioned above, resulting from differences in operatingconditions, including differences in operating environments.

The inventors have further recognized and appreciated that such ananalysis may be conducted using data generated by vehicular telemetrysystems of vehicles. Vehicular telemetry systems may include a hardwaredevice to monitor and log a range of vehicle parameters, componentparameters, system parameters and sub-system parameters in real time. Anexample of such a device is a Geotab® GO™ device available from Geotab,Inc. of Oakville, Ontario Canada (www.geotab.com). The Geotab® GO™device interfaces to the vehicle through an on-board diagnostics (OBD)port to gain access to the vehicle network and engine control unit. Onceinterfaced and operational, the Geotab® GO™ device monitors the vehiclebus and creates of log of raw vehicle data. The Geotab® GO™ device maybe further enhanced through an I/O expander (also available from Geotab,Inc.) to access and monitor other variables, sensors, devices,components, systems and subsystems resulting in a more complex andlarger log of raw data. Additionally, the Geotab® GO™ device may furtherinclude a GPS capability for tracking and logging raw GPS data. TheGeotab® GO™ device may also include an accelerometer for monitoring andlogging raw accelerometer data. The Geotab® GO™ o device may alsoinclude a capability to monitor atmospheric conductions such astemperature and altitude. The inventors thus recognized and appreciatedthat vehicle telemetry systems may collect types of data that, ifcombined with analysis techniques that analyze the data in a particularmanner, could be used to generate a reliable prediction of whetherand/or when an operational component will fail.

However, the inventors additionally recognized and appreciated that,when monitoring an operational component of a vehicle, that operationalcomponent may demonstrate significant variability in the signalsgenerated by the operational component and that would be monitored. Suchvariability presents an impediment to establishing clear analyses thatcould be used to determine whether a component is deteriorating orfailing. For example, while an operational component under idealoperating conditions may, while failing, generate an operationalparameter having a particular value, under non-ideal operatingconditions that same component might produce an operational parameterthat appears similar to that value associated with a failure, even whenthe operational component is not failing. Even for operationalcomponents that do not typically experience such a wide swing in valuesbetween conditions, the impact of variation in operating conditionsintroduces noise into a signal that substantially complicates analysisand prediction.

Generation of a reliable real-time prediction is further complicated byeffects of other operational components of the vehicle on a monitoredoperational component. In some events in which an operational componentmay be used, the operational component may interact with one or moreother operational components of the vehicle. The failure ordeterioration of these other operational components may affectoperational parameters generated by the operational component beingmonitored. This impact could cause signals to be generated by themonitored operational component that appear as if the operationalcomponent is deteriorating or failing, even in the case that theoperational component is not deteriorating or failing. Similarly,deterioration or failure of an operational component could be masked byits interaction with other operational components, or it may bedifficult to determine which operational component is deteriorating orfailing.

The inventors have thus recognized and appreciated that, in someembodiments, monitoring operating conditions of an operational componentmay aid in generating a reliable prediction of whether and/or when anoperational component will fail, or aid in increasing reliability ofsuch a prediction. Such operating conditions may include environmentalconditions, such as conditions in which a vehicle is being operated,including climate or weather conditions (temperature, humidity,altitude, etc.), characteristics of vehicle operation (e.g.,characteristics of acceleration, speed, braking, etc.), distancetraveled, loads carried, road conditions, or other factors thatinfluence operation of the vehicle. Operating conditions of anoperational component may additionally or alternatively includeinformation on other operational components of the vehicle, or ofmaintenance performed on operational components. Signals generated by anoperational component may be contextualized by that operating conditioninformation. The contextualization may aid in generating reliablepredictions of deterioration or failure, such as by eliminatingpotential noise or environment-triggered variation in operationalparameters.

Variation in operation signals may additionally be accounted for, ormitigated, in some embodiments by monitoring operational componentsthrough generation of statistical values that characterize operationalparameters generated by an operational component over time. Suchstatistical values may characterize an operational parameter in variousways, including describing a maximum value of a signal over a timeperiod, a minimum value of a signal over a time period, an average valueof a signal over a time period, a change in a signal over a time period,a variance of a signal over a time period, or other value that may becalculated from a statistical analysis of an operational parameter overtime. Different time periods may be used for calculating differentstatistical values. For example, some statistical values may becalculated from an analysis of values of an operational parametergenerated during a time period corresponding to one or more events inwhich the operational component performed an action, or interacted withother operational components of the vehicle to collectively perform anaction.

The inventors have further recognized and appreciated that additionalcomplexity may be introduced into monitoring of an operation componentby the number of different operational parameters that may be generatedby an operational component, and the number of statistical analyses thatcan be performed on these different operational parameters over time. Asmentioned in the preceding paragraph, in some embodiments, operationalparameters generated by an operational component specific to an eventmay be monitored and used to generate statistical values. Such an eventmay correspond to an action performed by one or more operationalcomponents of the vehicle. Over time, some operational components mayperform multiple different actions, and thus there may be a large numberof events that could be monitored. An operational component may engagein each action in a different way, or each action may have a differentimpact on an operational component. As a result, different operationalparameters may be generated. Moreover, when different operationalparameters are generated, there may be different characteristics of theoperational parameter that would be associated with proper operation,deterioration, or failure of the operational component. These differentcharacteristics may be reflected in different statistical analyses.Accordingly, identifying, even for one operational component, a mannerin which to analyze operational parameters to predict whether and/orwhen the operational component may fail is complex.

The inventors have recognized and appreciated that by monitoring a largegroup of vehicles, with the same or similar operation components, overtime, in different operating conditions, and collecting differentoperation signals over time, may enable selection of one or moreparticular events to monitor for an operational component, andparticular statistical analyses to perform of operational parametersgenerated during the event(s). Operational parameters collected foroperation components of the large group of vehicles may be analyzed,together with information on events that occurred at times the operationsignals were generated, to determine events and changes in operationalparameters that are correlated with deterioration or failure of anoperational component. For example, events and changes in operationalparameters that are correlated to different states of an operationalcomponent may be determined from the analysis. Based on identifiedcorrelations, one or more events to monitor and one or more statisticalanalysis to perform on operational parameters generated during theevent(s) may be determined. By identifying the event(s) and statisticalanalysis(es), a prediction process may be created based on the event(s)and the statistical analysis(es) that leverages the correlation and cangenerate a prediction of a state of an operation component whenoperational parameters from such an event are detected. Moreparticularly, for example, when a statistical analysis of operationalparameters from an event satisfy one or more conditions that, based onthe analysis of the operational parameters for the large group ofvehicles, is correlated with a deterioration of an operationalcomponent, the prediction process may determine that the operationalcomponent is deteriorating. As another example, when a statisticalanalysis of operational parameters from an event satisfy one or moreconditions that, based on the analysis of the operational parameters forthe large group of vehicles, is correlated with a failure of anoperational component, the prediction process may determine that theoperational component is failing.

Accordingly, described herein are techniques for collecting andanalyzing one or more operational parameters generated by one or moreoperational components during an event, and based on an analysis of theone or more operational parameters, generating a prediction of whetherand/or when a particular operational component will deteriorate or fail.Some techniques described herein may be used to determine, from ananalysis of the operational parameters, a current state of anoperational component, which may characterize how current operation ofthe operational component compares to operation of the operationalcomponent when new (e.g., whether the operational component has become“broken in,” or has begun deteriorating, or has significantlydeteriorated, or other suitable states that characterize a status of anoperational component).

In some such embodiments, operational parameters generated by a firstoperational component for which a prediction is generated may becontextualized in the analysis with other information. Such otherinformation may include operational parameters generated by one or moreother operational components at a time (e.g., during an event) that theoperational parameters of the first operational component weregenerated. Such other information may additionally or alternativelyinclude information on operating conditions of the vehicle. Such otherinformation may additionally or alternatively include information on amaintenance schedule of a vehicle and/or an operational component, suchas past completed maintenance (including repair or replacement) andplanned future maintenance.

In some embodiments, the vehicle may be a truck and the operationalcomponent may be a battery. Clearly, a battery is used over a longperiod of time and in connection with a large number of events.Operational parameters may be generated by the battery throughout thistime, and corresponding to any of the large number of events.Additionally, a large number of different statistical analyses could beperformed on these operational parameters. The inventors recognized andappreciated, however, that operational parameters generated during aparticular type of event may be useful in generating a prediction ofwhether the battery is deteriorating or failing, or when the batterywill fail. Moreover, the inventors recognized and appreciated thatanalyzing such operational parameters in the context of particularstatistical analyses, rather than analyzing the operational parametersdirectly, would yield reliable information on a state of the batterythat may be useful in predicting whether and/or when the battery willdeteriorate or fail.

In particular, the inventors recognized and appreciated that a startermotor event generates operational parameters that may be advantageouslyused in determining a status of a battery, and that evaluating a minimumand maximum voltage during a starter motor event and a variance of suchminimum voltages during starter motor events over time, may beadvantageous in generating a reliable prediction of whether and/or whenthe battery will fail. The inventors also recognized and appreciatedthat other components and parameters in association with the startermotor event may be beneficial to determining the status of a batterysuch as air temperature, oil temperature, coolant temperature, roadconditions (vibrations detected by an accelerometer) and altitudes.

During a starter motor event, the starter motor will draw energy fromthe battery. An operational parameter may be generated by the battery,or by a sensor that operates with the battery, that indicates a voltageof the battery over a time corresponding to the event. The event maylast from a time that energy starts being drawn from the battery for thestarter motor through a time that the engine of the vehicle has beensuccessfully started and an alternator is supplying electrical energy tothe battery. Over this time, the voltage of the battery may drop beforerising again once the battery is being charged by the alternator. Theoperational parameters for this event may indicate a voltage of thebattery over time, demonstrating the drop and then rise in voltage. Astatistical analysis may be performed for a starter motor event toidentify a maximum and minimum value of the voltage during the startermotor event. In addition, a statistical analysis may be performed formultiple starter motor events to calculate, over a period of time (e.g.,a number of starter motor events), a variance on the maximum and/orminimum voltage from individual starter motor events. An analysis ofthese maximum and minimum voltages and variances may be used to identifya state of the battery at a particular time, which describes howoperation of the battery at the time compares to operation of thebattery when new. The state of the battery may be useful in generating aprediction of whether and/or when the battery may fail, such as byidentifying lifespans of batteries in each of the states.

It should be appreciated that embodiment described herein may be used inconnection with any of a variety of vehicles and operational componentsof a vehicle. Embodiments are not limited to operating in connectionwith any particular operational component, any particular type ofoperational component, or any particular type of vehicle. Accordingly,while an example was given above of how the system may be used inconnection with an operational component that is a battery of a truck,and that example is used occasionally below to illustrate how aparticular technique may be implemented in some embodiments, it shouldbe appreciated that the example is merely illustrative and that otherembodiments may operate with other operational components or othervehicles. Accordingly, while specific examples of embodiments aredescribed below in connection with FIGS. 1-36, it should be appreciatedthat embodiments are not limited to operating in accordance with theexamples and that other embodiments are possible.

Vehicular Telemetry Environment & Raw Data Logging

Referring to FIG. 1 of the drawings, there is illustrated one embodimentof a high level overview of a vehicular telemetry environment andinfrastructure. There is at least one vehicle generally indicated at 11.The vehicle 11 includes a vehicular telemetry hardware system 30 and aresident vehicular portion 42. Optionally connected to the telemetryhardware system 30 is at least one intelligent I/O expander 50 (notshown in FIG. 1). In addition, there may be at least one wirelesscommunication module such as Bluetooth® wireless communication module 45(not shown in FIG. 1) for communication with at least one of thevehicular telemetry hardware system 30 or the intelligent I/O expander50.

The vehicular telemetry hardware system 30 monitors and logs a firstcategory of raw telematics data known as vehicle data. The vehiculartelemetry hardware system 30 may also log a second category of rawtelematics data known as GPS coordinate data and may also log a thirdcategory of raw telematics data known as accelerometer data.

The intelligent I/O expander 50 may also monitor a fourth category ofraw expander data. A fourth category of raw data may also be provided tothe vehicular telemetry hardware system 30 for logging as raw telematicsdata.

The Bluetooth® wireless communication module 45 may also be in periodiccommunication with at least one beacon such as Bluetooth® wirelesscommunication beacon 21 (not shown in FIG. 1). The at least oneBluetooth® wireless communication beacon may be attached or affixed orassociated with at least one object associated with the vehicle 11 toprovide a range of indications concerning the objects. These objectsinclude, but are not limited to packages, equipment, drivers and supportpersonnel. The Bluetooth® wireless communication module 45 provides thisfifth category of raw object data to the vehicular telemetry hardwaresystem 30 either directly or indirectly through an intelligent I/Oexpander 50 for subsequent logging as raw telematics data.

Persons skilled in the art appreciate the five categories of data areillustrative and only one or a suitable combination of categories ofdata or additional categories of data may be provided. In this context,a category of raw telematics data is a grouping or classification of atype of similar data. A category may be a complete set of raw telematicsdata or a subset of the raw telematics data. For example, GPS coordinatedata is a group or type of similar data. Accelerometer data is anothergroup or type of similar data. A log may include both GPS coordinatedata and accelerometer data or a log may be separate data. Personsskilled in the art also appreciate the makeup, format and variety ofeach log of raw telematics data in each of the categories is complex andsignificantly different. The amount of data in each of the categories isalso significantly different and the frequency and timing forcommunicating the data may vary greatly. Persons skilled in the artfurther appreciate the monitoring, logging and the communication ofmultiple logs or raw telematics data results in the creation of rawtelematics big data.

The vehicular telemetry environment and infrastructure also providescommunication and exchange of raw telematics data, information,commands, and messages between the at least one server 19, at least onecomputing device 20 (remote devices such as desktop computers, hand helddevice computers, smart phone computers, tablet computers, notebookcomputers, wearable devices and other computing devices), and vehicles11. In one example, the communication 12 is to/from a satellite 13. Thesatellite 13 in turn communicates with a ground-based system 15connected to a computer network 18. In another example, thecommunication 16 is to/from a cellular network connected to the computernetwork 18. Further examples of communication devices include Wi-Fi®wireless communication devices and Bluetooth® wireless communicationdevices connected to the computer network 18.

Computing device 20 and server 19 with corresponding applicationsoftware communicate over the computer network 18 may be provided. In anembodiment, the myGeotab™ fleet management application software runs ona server 19. The application software may also be based upon Cloudcomputing. Clients operating a computing device 20 communicate with themyGeotab™ fleet management application software running on the server19. Data, information, messages and commands may be sent and receivedover the communication environment and infrastructure between thevehicular telemetry hardware system 30 and the server 19.

Data and information may be sent from the vehicular telemetry hardwaresystem 30 to the cellular network 17, to the computer network 18, and tothe at least one server 19. Computing devices 20 may access the data andinformation on the servers 19. Alternatively, data, information, andcommands may be sent from the at least one server 19, to the network 18,to the cellular network 17, and to the vehicular telemetry hardwaresystem 30.

Data and information may also be sent from vehicular telemetry hardwaresystem to an intelligent I/O expander 50, to a satellite communicationdevice such as an Iridium® satellite communication device available fromIridium Communications Inc. of McLean, Va., USA, the satellite 13, theground based station 15, the computer network 18, and to the at leastone server 19. Computing devices 20 may access data and information onthe servers 19. Data, information, and commands may also be sent fromthe at least one server 19, to the computer network 18, the ground basedstation 15, the satellite 13, the satellite communication device, to anintelligent I/O expander 50, and to a vehicular telemetry hardwaresystem.

The methods or processes described herein may be executed by thevehicular telemetry hardware system 30, the server 19 or any of thecomputing devices 20. The methods or processes may also be executed inpart by different combinations of the vehicular telemetry hardwaresystem 30, the server 19 or any of the computing devices 20.

Vehicular Telemetry Hardware System Overview

Referring now to FIG. 2a of the drawings, there is illustrated avehicular telemetry hardware system generally indicated at 30. Theon-board portion generally includes: a DTE (data terminal equipment)telemetry microprocessor 31; a DCE (data communications equipment)wireless telemetry communications microprocessor 32; a GPS (globalpositioning system) module 33; an accelerometer 34; a non-volatilememory 35; and provision for an OBD (on board diagnostics) interface 36for communication 43 with a vehicle network communications bus 37.

The resident vehicular portion 42 generally includes: the vehiclenetwork communications bus 37; the ECM (electronic control module) 38;the PCM (power train control module) 40; the ECUs (electronic controlunits) 41; and other engine control/monitor computers andmicrocontrollers 39.

While the system is described as having an on-board portion 30 and aresident vehicular portion 42, it is also understood that this could beeither a complete resident vehicular system or a complete on-boardsystem.

The DTE telemetry microprocessor 31 is interconnected with the OBDinterface 36 for communication with the vehicle network communicationsbus 37. The vehicle network communications bus 37 in turn connects forcommunication with the ECM 38, the engine control/monitor computers andmicrocontrollers 39, the PCM 40, and the ECU 41.

The DTE telemetry microprocessor 31 has the ability through the OBDinterface 36 when connected to the vehicle network communications bus 37to monitor and receive vehicle data and information from the residentvehicular system components for further processing.

As a brief non-limiting example of a first category of raw telematicsvehicle data and information, the list may include one or more of but isnot limited to: a VIN (vehicle identification number), current odometerreading, current speed, engine RPM, battery voltage, cranking eventdata, engine coolant temperature, engine coolant level, acceleratorpedal position, brake pedal position, various manufacturer specificvehicle DTCs (diagnostic trouble codes), tire pressure, oil level,airbag status, seatbelt indication, emission control data, enginetemperature, intake manifold pressure, transmission data, brakinginformation, mass air flow indications and fuel level. It is furtherunderstood that the amount and type of raw vehicle data and informationwill change from manufacturer to manufacturer and evolve with theintroduction of additional vehicular technology.

Continuing now with the DTE telemetry microprocessor 31, it is furtherinterconnected for communication with the DCE wireless telemetrycommunications microprocessor 32. In an embodiment, an example of theDCE wireless telemetry communications microprocessor is a Leon 100™,which is commercially available from u-blox Corporation of Thalwil,Switzerland (www.u-blox.com). The Leon 100™ wireless telemetrycommunications microprocessor provides mobile communications capabilityand functionality to the vehicular telemetry hardware system 30 forsending and receiving data to/from a remote site 44. A remote site 44could be another vehicle or a ground based station. The ground-basedstation may include one or more servers 19 connected through a computernetwork 18 (see FIG. 1). In addition, the ground-based station mayinclude computer application software for data acquisition, analysis,and sending/receiving commands to/from the vehicular telemetry hardwaresystem 30.

The DTE telemetry microprocessor 31 is also interconnected forcommunication to the GPS module 33. In an embodiment, an example of theGPS module 33 is a Neo-5™ also commercially available from u-bloxCorporation. The Neo-5™ provides GPS receiver capability andfunctionality to the vehicular telemetry hardware system 30. The GPSmodule 33 provides the latitude and longitude coordinates as a secondcategory of raw telematics data and information.

The DTE telemetry microprocessor 31 is further interconnected with anexternal non-volatile memory 35. In an embodiment, an example of thememory 35 is a 32 MB non-volatile memory store commercially availablefrom Atmel Corporation of San Jose, Calif., USA. The memory 35 is usedfor logging raw data.

The DTE telemetry microprocessor 31 is further interconnected forcommunication with an accelerometer 34. An accelerometer (34) is adevice that measures the physical acceleration experienced by an object.Single and multi-axis models of accelerometers are available to detectthe magnitude and direction of the acceleration, or g-force, and thedevice may also be used to sense orientation, coordinate acceleration,vibration, shock, and falling. The accelerometer 34 provides this dataand information as a third category of raw telematics data.

In an embodiment, an example of a multi-axis accelerometer (34) is theLIS302DL™ MEMS Motion Sensor commercially available fromSTMicroelectronics of Geneva, Switzerland. The LIS302DL™ integratedcircuit is an ultra compact low-power three axes linear accelerometerthat includes a sensing element and an IC interface able to take theinformation from the sensing element and to provide the measuredacceleration data to other devices, such as a DTE TelemetryMicroprocessor (31), through an I2C/SPI (Inter-Integrated Circuit)(Serial Peripheral Interface) serial interface. The LIS302DL™ integratedcircuit has a user-selectable full-scale range of +−2 g and +−8 g,programmable thresholds, and is capable of measuring accelerations withan output data rate of 100 Hz or 400 Hz.

In an embodiment, the DTE telemetry microprocessor 31 also includes anamount of internal memory for storing firmware that executes in part,methods to operate and control the overall vehicular telemetry hardwaresystem 30. In addition, the microprocessor 31 and firmware log data,format messages, receive messages, and convert or reformat messages. Inan embodiment, an example of a DTE telemetry microprocessor 31 is aPIC24H™ microcontroller commercially available from Microchip TechnologyInc. of Westborough, Mass., USA.

Referring now to FIG. 2b of the drawings, there is illustrated avehicular telemetry hardware system generally indicated at 30 furthercommunicating with at least one intelligent I/O expander 50. In thisembodiment, the vehicular telemetry hardware system 30 includes amessaging interface 53. The messaging interface 53 is connected to theDTE telemetry microprocessor 31. In addition, a messaging interface 53in an intelligent I/O expander may be connected by the private bus 55.The private bus 55 permits messages to be sent and received between thevehicular telemetry hardware system 30 and the intelligent I/O expander,or a plurality of I/O expanders (not shown). The intelligent I/Oexpander hardware system 50 also includes a microprocessor 51 and memory52. Alternatively, the intelligent I/O expander hardware system 50includes a microcontroller 51. A microcontroller includes a CPU, RAM,ROM and peripherals. Persons skilled in the art appreciate the termprocessor contemplates either a microprocessor and memory or amicrocontroller in all embodiments of the disclosed hardware (vehicletelemetry hardware system 30, intelligent I/O expander hardware system50, wireless communication module 45 (FIG. 2c ) and wirelesscommunication beacon 21 (FIG. 2c )). The microprocessor 51 is alsoconnected to the messaging interface 53 and the configurablemulti-device interface 54. In an embodiment, a microcontroller 51 is anLPC1756™ 32 bit ARM Cortec-M3 device with up to 512 KB of program memoryand 64 KB SRAM, available from NXP Semiconductors Netherlands B.V.,Eindhoven, The Netherlands. The LPC1756™ also includes four UARTs, twoCAN 2.0B channels, a 12-bit analog to digital converter, and a 10 bitdigital to analog converter. In an alternative embodiment, theintelligent I/O expander hardware system 50 may include text to speechhardware and associated firmware (not illustrated) for audio output of amessage to an operator of a vehicle 11.

The microprocessor 51 and memory 52 cooperate to monitor at least onedevice 60 (a device 62 and interface 61) communicating with theintelligent I/O expander 50 over the configurable multi device interface54 through bus 56. Data and information from the device 60 may beprovided over the messaging interface 53 to the vehicular telemetryhardware system 30 where the data and information is retained in the logof raw telematics data. Data and information from a device 60 associatedwith an intelligent I/O expander provides the 4th category of rawexpander data and may include, but not limited to, traffic data, hoursof service data, near field communication data such as driveridentification, vehicle sensor data (distance, time), amount and/or typeof material (solid, liquid), truck scale weight data, driver distractiondata, remote worker data, school bus warning lights, and doorsopen/closed.

Referring now to FIGS. 2c, 2d and 2e , there are three alternativeembodiments relating to the Bluetooth® wireless communication module 45and Bluetooth® wireless communication beacon 21 for monitoring andreceiving the 5th category of raw beacon data. The module 45 includes amicroprocessor 142, memory 144 and radio module 146. The microprocessor142, memory 144 and associated firmware provide monitoring of beacondata and information and subsequent communication of the beacon data,either directly or indirectly through an intelligent I/O expander 50, toa vehicular telemetry hardware system 30.

In an embodiment, the module 45 is integral with the vehicular telemetryhardware system 30. Data and information is communicated 130 directlyfrom the beacon 21 to the vehicular telemetry hardware system 30. In analternate embodiment, the module 45 is integral with the intelligent I/Oexpander. Data and information is communicated 130 directly to theintelligent I/O expander 50 and then through the messaging interface 53to the vehicular telemetry hardware system 30. In another alternateembodiment, the module 45 includes an interface 148 for communication 56to the configurable multi-device interface 54 of the intelligent I/Oexpander 50. Data and information is communicated 130 directly to themodule 45, then communicated 56 to the intelligent I/O expander andfinally communicated 55 to the vehicular telemetry hardware system 30.

Data and information from a beacon 21, such as the Bluetooth® wirelesscommunication beacon provides the 5th category of raw telematics dataand may include data and information concerning an object associatedwith the beacon 21. In one embodiment, the beacon 21 is attached to theobject. This data and information includes, but is not limited to,object acceleration data, object temperature data, battery level data,object pressure data, object luminance data and user defined objectsensor data. This 5th category of data may be used to indicate, amongothers, damage to an article or a hazardous condition to an article.

Telematics Predictive Component Failure

Aspects disclosed herein relate to monitoring and optimally predictingreplacement or maintenance of a vehicle component before failure of thecomponent. Aspects disclosed herein also relate to monitoring andoptimally predicting replacement of an electrical or electronic vehiclecomponent before failure of the electrical component. By way of anexample only, the vehicle component may be a vehicle battery.

FIG. 3 illustrates a historical sample of raw big telematics data 200over a 14-month period of time. The sample is based upon a collection ofmultiple logs of data from the vehicular telemetry hardware system 30.The sample pertains to the use of a vehicle component over the usefullife of the vehicle component from a new installation, normal use,failure and replacement. The sample of raw big telematics data 200included 552 vehicle breakdowns based upon failure of a vehiclecomponent. The raw big telematics data 200 reveals operationalparameters around the process of vehicle component use and failure overseveral months of useful life. The raw big telematics data, orhistorical records of data, is obtained from at least one telematicshardware systems in the form of a log of data that is communicated to aremote site.

The y-axis is values of operational parameters for a vehicle componentbased upon a type of vehicle component event 211. For example, they-axis may be operational parameters for a vehicle battery during astarter motor cranking event where electrical energy is supplied by thevehicle battery to start an engine and then electrical energy isprovided back to the vehicle battery to replenish the energy used by thestarter motor cranking event (see FIG. 33 and FIG. 34). The x-axis isvalues relating to time over the life cycle of the vehicle component,for example days, months and years. In an embodiment, the raw bigtelematics data 200 illustrates the maximum and minimum values forvehicle component battery voltages for numerous starter motor crankingevents. The raw big telematics data 200 has two distinct patterns ortrends on either side of a vehicle component event 211 where this eventmay be either one of a failure event 210 or a maintenance event 220 withrespect to the vehicle component. The pattern prior to a vehiclecomponent event 211 includes a large or wide and increasing variation ofvalues on the y-axis (see FIG. 4 and FIG. 5). The pattern post a vehiclecomponent event 211 is a small or more narrow variation of values on they-axis.

Referring now to FIG. 4, a number of average values and variances overthe useful life of a vehicle component are generally indicated at 202.The average values and variances derived from the historical record ofraw big telematics data 200 reveals distinct operational patterns duringthe useful life of a vehicle component. A new vehicle component hasoperational parameters 203 that tend to have properties that are notablydifferent to the operational parameters of a failed vehicle component205. In the extreme, the magnitude of the new vehicle componentoperational parameters 203 is substantially higher than the magnitude ofa failed vehicle component operational parameters 205 and the varianceof new vehicle component operational parameters 203 is substantiallymore narrow than the variance of failed vehicle component operationalparameters 203.

The operational parameters evolve over time from a new vehicle componentstate to a failed vehicle component state wherein the magnitude of theoperational parameters decreases over time and the variance increasesover time until failure and installation of a new vehicle component.This is further illustrated in FIG. 5 as a superposition of the states(for example, new and failure). A new vehicle operational component willhave raw big telematics data 200 with operational parameters illustratedby 204. A few representative examples of operational components arevehicle batteries, starter motors, O2 sensors, temperature sensors andfluid sensors. Over continued use of the vehicle component, theoperational parameters will change or evolve where the raw bigtelematics data 200 will decrease in magnitude and increase in varianceas illustrated at 206. Essentially, the distribution of the operationalparameters flatten and widen over the operational useful life of thevehicle component. For an embodiment, the magnitude is a minimum voltagelevel based upon a vehicle component cranking event and the averageminimal cranking voltage decreases over time and operational useful lifeand the variance between minimal cranking voltage readings areincreasing. The vehicle component cranking event is an example of amaximum or significant operational load on the vehicle component incontrast to a minimal or lighter operational load on the vehiclecomponent. Referring now to FIG. 33 and FIG. 34, the voltage versus timeis illustrated for a good battery and a poor battery. FIG. 33illustrates the good battery cranking event voltage curve. When thevehicle ignition key is activated, the voltage starts to decreaseslightly followed by a very steep drop in the voltage. Then, after thecranking event has been completed, the voltage rises on a recharge slopewithin a dwell time where the voltage reaches a steady state forrecharging the battery. FIG. 34 illustrates the poor battery crankingevent voltage curve. The initial voltage is lower for the poor battery.When the vehicle ignition key is activated, the voltage starts todecrease slightly followed by a very steep drop in the in the voltage.Then, after the cranking event has been completed, the voltage rises ona more shallow recharge slope within a longer dwell time where again thevoltage reaches a steady state for recharging the battery.

FIG. 6 further illustrates at 207 the degradation of vehicle componentoperational parameters over the useful life cycle of a vehicle componentfrom a new vehicle component to a failed vehicle. There are five statesthat represent the life cycle being a new component state, a goodcomponent state, a fair component state, a poor component state and afailed component state indicated by a failure event 210. The averageoperational parameter of the vehicle component decreases over the lifecycle time and the variation increases over the life cycle time of thevehicle component. Since the raw big telematics data 200 represents asimilar or same vehicle component installed in many vehicles withdifferent or similar operational environments (altitudes, temperatures,vibration and frequency of use), operational parameters, variationsmonitoring indicators and signal monitoring indicators may be derivedfrom the sample of raw big telematics data 200 and associated with thedifferent states from a new vehicle component to a failed vehiclecomponent. The operational parameters, variations, monitoring indicatorsand signal monitoring indicators and different states may also beassociated with a known event 210, for example failure of the vehiclecomponent.

FIG. 7 is a first framework with derived failure process control limits214 to filter the raw big telematics data 200 and extract patterns ofdata that are indicative and representative of vehicle componentoperational states (new, good, fair, poor and fail). The frameworkestablishes a monitor to view the filtered raw big telematics data andidentify an operational process change indicative and representative ofeach vehicle component operational state over time. A sample or portionof the raw big telematics data 200 may be transformed intorepresentative signals 216. The representative signals 216 may beassociated with a normal distribution 212 viewed at right angles to thesignals 216. The normal distribution 212 provides an indication of asteady process or a changing process with respect to the signals 216.The outer boundaries or limits for the signals 216 are the upper controllimit (UCL) 252 and the lower control limit (LCL) 264 on either side ofthe mean 258. In addition, there are a series of intermediate limitsbetween the boundaries. The intermediate limits relate to differentfactors of a standard deviation (SD) (plus/minus 1 SD, 2 SD, 3 SD). Thesignals 216 and location of the signals 216 within the framework withrespect to the mean, outer limits and intermediate limits provide anindication to the vehicle component where the operational state may bechanging, decreasing as the vehicle component approaches failure orincreasing if the vehicle component has received maintenance orreplacement.

The raw big telematics data 200 representative of the vehicle componentoperational life cycle of FIG. 3 may be filtered to smooth outshort-term fluctuations and highlight longer-term trends in the lifecycle data. This is illustrated in FIG. 8. The raw big telematics data200 is filtered to provide a moving average 218 derived from the raw bigtelematics data 200. Alternatively the moving average could be ranges ofthe data, averages of the data or the result of a low pass or impulsefilter. In addition to the raw big telematics data 200 that ismonitored, log and stored, additional vehicle component event 211 datais also provided. Vehicle component event 211 data is typically sourceddifferently and separately from the raw big telematics data 200 but mayalso be sourced with the raw big telematics data 200. When sourceddifferently and separately, the vehicle component event 211 data isobtained from maintenance records or a vehicle maintenance database. Thevehicle component event 211 data may include the type of event, the dateof the event and time of the event. Vehicle component event 211 dataincludes at least one of either a failure event 210 or a maintenanceevent 220 concerning the vehicle component. The vehicle component event211 data defines a known event with respect to the vehicle component andis associated with the moving average 218 representative of the raw bigtelematics data 200. Individual values or data points of the movingaverage 218 data are steadily decreasing over time up to the point ofthe vehicle component event 211. Immediately after the vehicle componentevent 211, the individual values or data points of the moving average218 data sharply increase over a shorter period of time and thenmaintain a relatively consistent moving average 218 going forward intime. The different patterns of the moving average 218 data areindications of a process change between a vehicle component good state,a poor state, a failed state, a new state and/or a refurbished state.

Referring now to FIG. 9, the graph of FIG. 8 may be further supplementedwith a number of first component or monitoring indicators 222 ormonitoring indicators. The first component indicators 222 are derivedfrom the raw big telematics data 200 and include a mean indication, apair of 1^(st) standard deviation indications (plus/minus 1 SD), a pairof 2^(nd) standard deviation indications (plus/minus 2 SD), the uppercontrol limit (UCL) indication and the lower control limit (LCL)indication. The first component indicators 222 are in relationship tothe derived moving average 218.

Referring now to FIG. 10, the graph of FIG. 9 may be furthersupplemented with a number of different signals applied as signalmonitoring indicators. The different signals are also derived from theraw vehicle component life cycle data 200. FIG. 18 is also an enlargedview of the signal monitoring indicators 222 and includes a plurality oflike and different signals. A first “R” signal 234 is derived and existswhen a single data point is above the upper control limit (UCL) 252 orbelow the lower control limit (LCL) 264. This “R” signal, or when morethan one of these “R” signals occur in a time based sequence, isindicative a change in state of a vehicle component. A second “Y” signal228 is also derived from the raw vehicle component data 200 and existswhen a series of 8 consecutive data points are on the same side of themean either between the mean and the upper control limit (UCL) 252 orbetween the mean 258 and the lower control limit (LCL) 264. The “Y”signals are also indicative of a change in state of a vehicle component.The third “O” signal 230 is also derived from the raw vehicle componentdata 200 and exists when a series of 4 out of 5 consecutive data pointsare either between the mean 258 and greater than one sigma (1 SD) 256 orbetween the mean 258 and greater than minus one sigma (1 SD) 260. Again,the “O” signals are also indicative of a change in state of the vehiclecomponent. Finally, a fourth “B” signal 232 is derived from the rawvehicle component data 200 and exists when a series of 2 out of 3consecutive data points are either between the mean 258 and greater thantwo sigma (2 SD) 254 or the mean 258 and greater than minus two sigma (2SD) 262. The “B” signals are also indicative of a change in state of thevehicle component.

FIG. 11 is an alternative embodiment to FIG. 9. The graph of FIG. 11 issupplemented with a number of second component indicators 224. Thesecond component indicators 224 are derived from the raw vehiclecomponent life cycle data 200 and include the six ranges between theupper control limit (UCL) 252 and the lower control limit (LCL) 264 andtwo ranges below the lower control limit (LCL) 264. The second componentindicators 224 are different from the signal monitoring indicators 222.Signal monitoring indicators 222 are statistical limits for measuring orgauging the different signals associated with a component over theuseful operational life of the vehicle component. Second componentindicators 224 are based upon statistical limits but may be alone or incombination to provide a real time status indication (new, good, poor,fair) concerning the vehicle component.

FIG. 12 is an alternative embodiment to FIG. 10. The graph of FIG. 12includes the different types of signal monitoring indicators incombination with the second component indicators 224.

FIG. 13 is an alternative embodiment and includes the different types ofsignal monitoring indicators in combination with a third set ofcomponent monitoring indicators 226. The third set of componentmonitoring indicators 226 includes 4 categories. The first category is“New”. New relates to derived signals 216 between a value for the uppercontrol limit (UCL) 252 and 2 SD 254. The second category is “Good”.Good relates to derived signals 216 between a value of positive 2 SD 254and minus 2 SD 262. “Fair” is a third category and relates to derivedsignals 216 between a value of minus 2 SD 262 and the lower controllimit (LCL) 264. Finally, a fourth category is “Poor” and this fourthcategory relates to derived signals 216 below the lower control limit(LCL) 264.

Telematics Predictive Component Failure Signals, Limits and Data

FIG. 14 illustrates a framework for predictive vehicle component failureincluding a real sample of raw vehicle component life cycle data 200, aplurality of different derived signals 216, a monitoring indicator 236and a vehicle component event 211. The monitoring indicator 236 may beone of or a combination of the first component monitoring indicators222, second component monitoring indicators 224 or the third componentmonitoring indicators 226. The framework also includes the upper controllimit (UCL) 252, the lower control limit (LCL) 262 and the mean 258. Theplurality of derived signals 216 may include one or more of an “R”signal, a “B” signal, an “O” signal or a “Y” signal (see FIG. 17).

Referring now to FIG. 15, the framework for predictive vehicle componentfailure of FIG. 14 further includes a number of identified zones orareas when the derived signals 216 exhibit similar patterns. A firstzone 240 exists where the mix of derived signals 216 include “B”signals, “O” signals and “Y” signals. All of the derived signals 216 inthe zone 240 exist between the mean 258 and an upper control limit (UCL)252. The first zone 240 is indicative of a particular vehicle componentstate, for example relatively new vehicle component with relatively newoperating parameters.

A second zone 242 also exists. The mix and grouping of the derivedsignals 216 generally include “Y” signals and “O” signals with a smallernumber of “B” signals. The derived signals 216 are predominately betweenthe mean 258 and the upper control limit (UCL) 252 with a few or smallnumber of derived signals 216 existing below the mean 258 and the lowercontrol limit (LCL) 264. The second zone 242 generally spans six toeight months of vehicle component use. The second zone 242 is indicativeof a particular vehicle component state, for an example good vehiclecomponent with good operational parameters.

A third zone 244 exists. The mix and grouping of the derived signals 216include “Y” signals, “O” signals, “B” signals and “R” signals. There isa mix of “Y” signals and “O” signals both above and below the mean. The“B” signals are below the mean 258 and above the lower control limit(LCL) 264. There is an introduction of “R” signals. The third zone 244is approximately 1.5 months in duration of vehicle component use. Thethird zone 244 is indicative of a particular vehicle component state,for example a fair vehicle component with fair operating parameters.

A fourth zone 246 exists where the mix and grouping of the derivedsignals 216 include “Y” signals, “O” signals and “B” signals. The mix ofderived signals 216 are between the mean 258 and the lower control limit(UCL) 264. The fourth zone 246 is approximately 1 month in duration ofvehicle component use. The fourth zone 246 is indicative of a particularvehicle component state, for example a poor vehicle component with pooroperating parameters.

Directly below the fourth zone for approximately the same duration (1month of vehicle component use) is a fifth zone 248. The fifth zone is agrouping of “R” signals. The “R” signals are below the lower controllimit (LCL) 264. The “R” signals have a less dense grouping at thebeginning of the fifth zone 248 and a more dense grouping at the end ofthe fifth zone 248. The individual value of each “R” signal is alsodiminishing with a significant increase in variation just before thefailure event 210. Alternatively, the fourth zone and the fifth zone maybe combined into one zone indicative of a poor vehicle component leadingto a failed vehicle component.

After the vehicle component event 211, in this example, the maintenanceevent 220 where a new vehicle component has been installed, there is asixth zone 250. The sixth zone 250 is a mix and grouping of “Y” signals,“O” signals, “B” signals and “R” signals. The “R” signals are at orabove the upper control limit (UCL) 252. The “Y” signals, “O” signalsand “B” signals are between the mean 258 and upper control limit (UCL)252. The number of “B” signals are much more prevalent in the sixth zone250 when compared to the other zones. This is indicative of a newvehicle component with new operating parameters.

Referring now to FIG. 16, a failure curve 238 is provided with theframework for predictive vehicle component failure. The slope of thefailure curve 238 is relatively flat, or a slightly negative valuethroughout the second zone 242. The slope of the failure curve 238towards the end of the third zone 244 starts to increase in the negativedirection as it enters the fourth zone. The slope of the failure curve238 in the fourth zone 246 and fifth zone 248 significantly increases inthe negative direction until the vehicle component event 211.Immediately after the vehicle component event 211, the slope of thefailure curve 238 rapidly increases in the positive direction.

FIG. 17 is an enlarged representation of the third zone 244, fourth zone246, fifth zone 248 and sixth zone 250 of the framework for predictivevehicle component failure. In the third zone 244, the moving average 218is relatively constant. The moving average 218 is between the mean 258and minus one SD 260. There are a number of derived signals 216 aboveand below the moving average 218. The derived signals 216 above andbelow the moving average 218 include “Y” signals and “O” signals. Thereare more “Y” signals 228 than “O” signals. There are a few “B” signalsbelow the moving average and a few “R” signals below the lower controllimit (LCL) 264. The variance in the third zone is larger than thevariance in the first zone 240 and second zone 242. For clarity, thedifferent signals may be coded as different colors to represent thedifferent signals on a computer or display screen. For example, thoughthe various signals are represented as varying stipple density rangingfrom none to solid, on a computer screen, the “Y” signal may berepresented in yellow, the “O” signal may be represented in orange, the“B” signal may be represented in blue and the “R” signal may berepresented in red.

In the fourth zone 246, the moving average 218 is decreasing through thezone. It should be appreciated that as used herein, the phrase“increasing in the negative direction”, “negatively increasing”,“decreasing”, “declining” and “move downward” may be usedinterchangeably. The moving average begins close to the mean 258 andmoves downwardly towards minus 2 SD 264 at the end of the fourth zone246. There are a number of “Y” signals above and below the movingaverage 218. The number of “Y” signals and the number of “B” signals arehigher in number when compared to the third zone 244. The number of “O”signals are relatively the same number when compared to the third zone244.

In the fifth zone 248, there is a significant number of “R” signals whencompared to the third zone 244. When examining the derived signals 216in the fourth zone 246 and fifth zone 248, the variance is the largestwhen compared to the other zones.

In the sixth zone 250, the moving average 218 starts with a steep andsignificant positive slope at the beginning of the zone. The movingaverage 218 then becomes relatively consistent around plus 1S 256. Thereare a few “R” signals at or above the upper control limit 252. There area number of “B” signals, “Y” signal and “O” signals predominantly abovethe moving average. There are a large number of “B” signals and “Y”signals with less “O” signals. All of the derived signals 216 are abovethe moving average 218 at the portion of the moving average 218 that hasthe steep and significant positive slope at the beginning of the zone.The variation becomes small and significantly less when compared to thevariation of the fourth zone 246 and fifth zone 248. This change ofstate and the associated data and signals occurs within a time frame ofapproximately one month.

Telematics Predictive Component Failure Categories

The telematics predictive component failure categories are introducedwith respect to FIG. 18. This figure illustrates a small portion of theoperational data over the useful life of a measured vehicle component.The data is also associated with 552 vehicle component events 211, forexample a breakdown, a failure, maintenance or replacement of thevehicle component. The data includes different signals derived from thedata as generated by a component operational event. An example of acomponent operational event is as a starter motor cranking event where avehicular telemetry hardware system 30 has logged the minimum crankingvoltage over the useful life of a vehicle component, for example abattery. The vehicular telemetry hardware system 30 has also logged thebattery voltages over the useful life of the battery. The data prior tothe vehicle component event 211 and post the vehicle component event 211includes many different signals derived from the data. These signalshave been previously described. In order to predict a vehicle componentevent 211, it may be helpful to have consistent signals and patterns.Patterns of signals are indicative of a change in the vehicle componentoperational state. For example, consistent patterns of signals reveal aprocess voltage change when a battery has failed, or has been replaced.In this example embodiment, a moving average 218 (voltage) may bedecreasing rapidly prior to the vehicle component event 211 coupled witha very low minimum cranking voltage set of signals just prior to thevehicle component event 211 indicating a vehicle component state. Themoving average 218 may be significantly increasing after the vehiclecomponent event 211 indicating another vehicle component state. Themoving average voltage 211 may be relatively constant, not decreasingprior to the vehicle component event 211 and not increasing post thevehicle component event 211 indicating yet another vehicle componentstate or a particular type of vehicle component event 211.

In an embodiment, the vehicle component event is a vehicle starter motorcranking event. The cranking event is determined when the vehiculartelemetry hardware system 30 detects a 0.7 voltage decrease over a 100ms period of time. This then triggers the vehicular telemetry hardwaresystem 30 to request the engine RPM over the vehicle bus. If the RPM isgreater than 200 RPM, a cranking event is logged. Alternatively, thevoltage decrease triggers the vehicular telemetry hardware system 30 torequest the speed either through the vehicle bus, or through a GPSmodule 33. A positive indication of speed indicates a cranking eventthat is logged. Optionally, there may be a delay in requesting the speedfor the situation where the vehicle is momentarily in a reversedirection (zero speed on the vehicle bus). Alternatively, the voltagedecrease triggers the vehicular telemetry hardware system 30 to senseacceleration through an accelerometer 34 and a positive indication ofacceleration in either the forward or reverse direction provides anindication to a cranking event that is logged.

Referring now to FIG. 19, a first vehicle component breakdown ormaintenance event is described with respect to a vehicle componentfailure that resulted in a vehicle breaking down, for example with adead battery. For the sake of clarity, the complete set of signals fromFIG. 18 is reduced to focus on a moving average 218 (voltage) andsignals (voltages) indicative of a vehicle component event 211 with avehicle component state of failure. The moving average 218 (voltage) andsignals make it possible to predict failure and provide a recommendationto replace a vehicle component prior to actual failure of the component.Initially, the moving average 218 (voltage) remains relatively constantaround, or above and below the mean 258. Before the vehicle componentevent 211, the moving average 218 (voltage) decreases over time as thevehicle component becomes increasingly weaker. In addition to the movingaverage 211 characteristics, the lower cranking voltage values from avehicle cranking event are producing an increasing number of derivedsignals just prior to the vehicle component event 211. The “Y” signals228 (voltage) provide a pattern below the moving average 218 (voltage).In addition, there is an increasing appearance, or clustering orgrouping of “R” signals 234 (voltage). The “R” signals 234 (voltage) arebelow the lower control limit 264 and over time move further away fromthe lower control limit 264. The “R” signals 234 (voltage) may befurther assessed based on a distance 310 of each signal from the lowercontrol limit 264 to further reveal and predict a component failureprior to the actual component failure event 210. Another monitoringindicator (not shown) may be applied to the “R” signals 234 and therespective signal distance 310 from the lower control limit 264 for eachsignal to further define the accuracy and prediction of an imminentvehicle component failure. In an embodiment, this monitoring indicatorincludes two areas with two different shades of red (not shown) whereinvehicle component replacement is recommended when the “R” signals 234enter the lower section (e.g., the darker shade of red section).Immediately after the vehicle component event 211, the moving average218 (voltage) sharply increases over time indicating the vehiclecomponent has been replaced and the vehicle component is operating in anew vehicle component state. A further indication of a vehicle componentevent 211 is that the moving average 218 (voltage) is at a higher value(above the mean 258) post the vehicle component event 211 when comparedto the moving average 218 (voltage) operating a lower value prior to thevehicle component event 211.

Referring now to FIG. 20, a second vehicle component event is describedwith respect to a vehicle component event 211 that was not related tothe particular vehicle component being monitored. Again, the completeset of signals from FIG. 18 are reduced for clarity purposes to focus oneither side of the vehicle component event 211. The moving average 218(voltage) is relative constant around the mean 258 on both sides of thevehicle component event 211 (prior and post). There is no significantincrease or decrease in the moving average 211 post the vehiclecomponent event 211. In addition to the moving average 211characteristics, there are very few “R” signals 234. The “R” signals 234are not grouped or clustered on either side of the vehicle componentevent 211. There is no significant change in the voltage values at thetime of the vehicle component event 211.

Referring now to FIG. 21, a third vehicle component event is describedthat resulted in a premature replacement of the vehicle component. Thisform of vehicle component event analysis is useful in identifyingpremature replacement of vehicle components from a vehicle maintenanceperspective. Prior to the vehicle component event 211, the movingaverage 218 (voltage) is relative constant around the mean 258. There isno significant decrease in the moving average 211 (voltage) prior to thevehicle component event 211. Post the vehicle component event 211, themoving average 218 (voltage) has an increase to a level above the mean258, in this example approximately at 1 SD 256. There are few “R”signals 234 and certainly no grouping or clustering of “R” signals 234.These characteristics indicate that a vehicle component was replacedwell before any failure where the vehicle component still had a usefullife and use before failure.

Referring now to FIG. 22, a fourth vehicle component event is describedthat resulted in an incorrect recording of the vehicle component event211. FIG. 22 is the same as FIG. 19 with respect to the moving average218 (voltage), the “Y” signals 228 and the “R” signals 234. However, thevehicle component event 211 does not line up with either the “R” signals234, the decreasing value of the moving average 218 (voltage) prior tothe vehicle component event 211, or the increasing value of the movingaverage 218 (voltage) post the vehicle component event. The movingaverage 218 (voltage) is significantly increasing just before thevehicle component event 211 and the moving average 218 (voltage) isrelatively constant post the vehicle component event 211. The vehiclecomponent event 211 does not coincide with a change in the movingaverage 218 (voltage) and “R” signals 234. These characteristicsindicate that a vehicle component event 211 was not properly oraccurately recorded with respect to the date and time of the event.

Referring now to FIG. 23, a fifth vehicle component breakdown ormaintenance event is described with respect to a maintenance eventconcerning the vehicle component. For this example, the vehicle 11 hasbeen in a stationary inactive state or event for an extended period oftime as reflected in the log (several days or several months forexample). There is no significant decrease in the moving average 211(voltage) prior to the vehicle component event 211. Prior to the vehiclecomponent event 211, the moving average 218 (voltage) has an increase toa level above the mean 258. After the vehicle component event 211 andthe corrective action of maintenance, the moving average 218 isapproximately the same in value prior to the vehicle component event211. There are few “R” signals 234 and certainly no grouping orclustering of “R” signals 234 around the vehicle event 211. Thesecharacteristics indicate that a vehicle component was repaired, or inthe example where the vehicle component is a battery, the battery wasboosted.

Referring now to FIG. 24, a vehicle component rating framework isdescribed that provides an indication of the operational state of thevehicle component. In an embodiment, the framework includes the uppercontrol limit 252, plus 2 SD 254, plus 1 SD 256, the mean 258, minus 1SD 260, minus 2 SD 262 and the lower control limit 264. The movingaverage 218 is located within the framework as well as a number of “R”signals 234. Alternatively, other signals could be included within theframework. The vehicle component event 211 is also included within theframework with a failure event 210 and a maintenance event 220. Acomponent monitoring indicator 226 is also included with the framework.The component monitoring indicator 226 is divided up into zones orregions representative of component states. A first region of thecomponent monitoring indicator 226 is a new state 320. The new state 320could be represented by downwardly sloping cross-hatched lines or a darkgreen color if on video display screen. This occurs for component valuesbetween plus 0.5 SD and plus 2 SD 254. A second region of the componentmonitoring indicator 226 is a good state 330. The good state 330 couldbe represented by upwardly sloping cross-hatched lines or a light greencolor if on video display screen. The second region is for componentvalues between 0.5 SD and minus 1 SD 260. A third region of thecomponent monitoring indicator 226 is a fair state 340. The fair state340 could be represented by downwardly sloping cross-hatched lines or ayellow color if on video display screen. The third region is forcomponent values between the minus 1 SD 260 and the lower control state264. Below the lower control state 264 is a fourth region of thecomponent monitoring indicator 226. The fourth region includes twosub-sections. The first sub-section indicates a poor state 350 a and thesecond sub-section indicates a failed state 350 b. The poor state 350 acould be represented by upwardly sloping cross-hatched lines or a lightred color if on video display screen and the failure state 350 b couldbe represented by upwardly sloping cross-hatched lines or a dark redcolor if on video display screen.

The first region, second region and third region relate to the movingaverage 218 to provide an indication of component rating. When themoving average 218 is disposed within the first region, the componentstate is identified as new. When the moving average 218 is disposedwithin the second region, the component state is identified as good.When the moving average 218 is disposed within the third region, thecomponent state is identified as fair. When the moving average 218 isdecreasing over time from the second region to the third region, thecomponent state is changing from a good state to a fair state. When themoving average 218 is increasing over time from the third region, oralternatively the second region to the first region, the component stateis changing to a new state. Alternatively, the grouping and pattern ofsignals (see FIGS. 15, 16, and 17) and the trends of the signals higheror lower in the framework also provide an indication to the componentstate. In and embodiment, the moving average 218 provides the indicationto component state (new, good, fair). In another embodiment, the signalsprovide an indication to the component state (new, good, fair).

Signals below the lower control limit 264 provide an indication betweena component state of poor and eventually failure. In an embodiment, whenthe grouping of the “R” signals 234 is close to the lower control limit264, they provide an indication of a poor component state. As thesignals or a grouping of the “R” signals 234 move further away from thelower control limit 264, they provide an indication of a failurecomponent state up to the point of component failure 210. In analternative embodiment, the “R” signals 234 and the moving average 218provide an indication of either a poor or failure component state. Inthis embodiment, the moving average 218 is between minus 1 SD 260 andminus 2 SD 262 with “R” signals disposed beneath this section of themoving average 218.

In summary, telematics predictive component failure frameworks andcategories of component states are enabled through distinct patterns andvalues revealed in the derived data from the raw telematics data 200captured over the life cycle of a vehicle components useful life. Forthe example of a vehicle battery, voltages, the moving average 218 andderived signals show distinct patters prior to and after a componentevent 211. Voltage readings are decreasing with less recovery andindividual voltage events are creating signals prior to the componentevent 211 of a failure event 210. After the component event 211,assuming a maintenance event 220, higher voltage levels are indicatedwhen a battery has been replaced with a new battery.

Telematics Predictive Component Failure Data

Referring now to FIG. 25, the different types of raw telematics datauseful alone or in combination for predictive component failure andmaintenance validation are described. The vehicular telemetry hardwaresystem 30 has the capability to monitor and log many different types oftelematics data to include GPS data, accelerometer data, vehiclecomponent data (data specific to the component being assessed forpredictive failure or maintenance validation), vehicle data and vehicleevent data. In addition, event data may be supplemented to the log ofraw telematics data provided by the vehicular telemetry hardware system30. The predictive component failure analysis process uses the rawtelematics data and event data to provide a predictive component failureindication, a recommendation for maintenance and validation orindication of a maintenance activity.

The GPS module 33 provides GPS data in the form of latitude andlongitude data, time data and speed data that may be applied to indicatemotion of a vehicle. The accelerometer 34 provides accelerometer datathat may be applied to indicate forward motion or reverse motion of thevehicle.

Vehicle data includes the first category of raw telematics vehicle dataand information such as a vehicle component type or identification,vehicle speed, engine RPM and two subsets of data. The first subset ofdata is the vehicle component data. Vehicle component data is specificparameters monitored over the life cycle and logged for a particularvehicle component being assessed for predictive component failure. Forexample, if the vehicle component if a vehicle battery, then raw batteryvoltages and minimum cranking voltages are monitored and logged. Thesecond subset of data is vehicle event data. This may be a combinationof vehicle data applied or associated with a vehicle event or a vehiclecomponent event. For example, if the vehicle component is a vehiclebattery and the event is a cranking event, then the vehicle data eventmay include one or more of ignition on data, engine RPM data, degreasein battery voltage data, speed data and/or accelerometer data.

Event data typically includes a record of a vehicle event. This mayinclude one or more of a maintenance event, a repair event or a failureevent. For example, with a vehicle battery the maintenance event wouldbe a record of charging or boosting a battery. A repair event would be arecord of replacing the battery. A failure event would be a record of adead battery. Event data typically includes a date and time associatedwith each event.

Telematics Predictive Component Event Determination Process

Referring now to FIG. 26, the predictive component failure analysisprocess is described. The predictive component failure analysis processis generally indicated at 500. This process and logic may be implementedin a server 19 or in a computing device 20 or in a vehicular telematicshardware system 30 or a combination of a server, computing device andvehicular telematics hardware system. This process may also beimplemented as a system including a vehicular telematics hardware system30 and a remote device 44. Finally, this process may also be implementedas an apparatus that includes a vehicular telematics hardware system 30.The process begins by receiving historical data. The historical dataincludes vehicle event data and raw telematics data 200. The rawtelematics data 200 includes vehicle component data. The vehiclecomponent data includes vehicle component data before one or morevehicle events and after one or move vehicle events. Vehicle componentdata is the historical operational data obtained over time from avehicular telemetry hardware system 30 (see FIG. 1). Vehicle componentdata includes operational data for at least one vehicle component.Vehicle component data is also the life cycle data for a component froma new installation to failure situation. Alternatively, vehiclecomponent data is from a good operational component to a failedoperation component.

Vehicle component data includes operational component data from at leastone type of vehicle based upon fuel based vehicles, hybrid basedvehicles or electric based vehicles. The broad categories include: fueland air metering, emission control, ignition system control, vehicleidle speed control, transmission control and hybrid propulsion. Thesebroad categories are based upon industry OBDII fault or trouble codeseither generic or vehicle manufacturer specific. The vehicle componentdata may include one or more data generated by thermostat or temperaturesensors (oil, fuel, coolant, transmission fluid, electric motor coolant,battery, hydraulic system), pressure sensors (oil, fuel, crankcase,hydraulic system), or other vehicle components, sensors or solenoids(fuel volume, fuel shut off, camshaft position, crankshaft position, O2,turbocharger, waste gates, air injections, mass air flow, throttle body,fuel and air metering, emissions, throttle position, fuel delivery, fueltiming, system lean, system rich, injectors, cylinder timing, enginespeed conditions, charge air cooler bypass, fuel pump relay, intake airflow control, misfire (plugs, leads, injectors, ignition coils,compression), rough road, crankshaft position, camshaft position, enginespeed, knock, glow plug, exhaust gas recirculation, air injection,catalytic convertor, evaporative emission, vehicle speed, brake switch,idle speed control, throttle position, idle air control, crankcaseventilation, air conditioning, power steering, system voltage, enginecontrol module, throttle position, starter motor, alternator, fuel pump,throttle accelerator, transmission control, torque converter,transmission fluid level, transmission speed, output shaft speed, gearpositions, transfer box, converter status, interlock, torque,powertrain, generator, current, voltage, hybrid battery pack, coolingfan, inverter and battery).

An example of vehicle component data is battery voltages duringoperational use of a vehicle battery or battery voltages based upon acranking event. The cranking event produces a minimum battery voltagefollowed by a maximum battery voltage as the battery is recharging toreplace the energy used by a vehicle starter motor.

The vehicle event data typically includes a date, or date and time, andthe type of vehicle event. The type of vehicle event may be failure,maintenance or service. For example, a failure of a vehicle battery iswhen the vehicle would not start. Maintenance of a vehicle battery couldbe replacement of the vehicle battery. Service of a vehicle batterycould be a boost.

For each vehicle component under analysis, the moving average 218 fromthe vehicle component data to filter the vehicle component data isdetermined. Alternatively, an average moving range or median movingrange could be determined.

One example of determining the upper control limit 252 from the vehiclecomponent data will now be described. In an embodiment, the uppercontrol limit 252 is determined from the vehicle component data bymultiplying the moving range by 3.14 and adding it to the average. Inanother embodiment, the upper control limit 252 is determined from thevehicle component data by multiplying the average moving range by 2.66and addition it to the average. In another embodiment, the upper controllimit 252 is determined from the vehicle component data by multiplyingthe median moving range by 3.87. In another embodiment, the uppercontrol limit 252 is determined from the vehicle component data bymultiplying the average moving range by 3.27.

One example of determining the lower control limit 264 from the vehiclecomponent data will now be described. In an embodiment, the lowercontrol limit 264 is determined from the vehicle component data bymultiplying the moving range by 3.14 and subtracting it from theaverage. In another embodiment, the lower control limit 264 isdetermined from the vehicle component data by multiplying the averagemoving range by 2.66 subtracting it from the average. In anotherembodiment, the lower upper control limit 264 is determined from thevehicle component data by multiplying the median moving range by 3.87.In another embodiment, the lower control limit 264 is determined fromthe vehicle component data by multiplying the average moving range by3.27.

Persons skilled in the art appreciate the constants of 3.14, 3.87, 2.66and 3.27 are scaling factors needed to convert the data into appropriateoperational limits and that other scaling factors may be applied todetermine the upper control limit and the lower control limit from thevehicle component data.

One example of determining from the vehicle component data, a plus andminus standard deviation (256, 260), and a plus and minus two standarddeviations (254, 262) will now be described. The determined uppercontrol limit 264, the lower control limit 264, the plus and minusstandard deviation (256, 260) and the plus minus two standard deviations(254, 262) are the determined set of first component monitoringindicators 222. Alternatively, the areas intermediate each componentmonitoring indicators 222 could be applied as a second set of componentmonitoring indicators 224. Alternatively, either the first set ofcomponent monitoring indicators 222 or the second set of componentmonitoring indicators 224 could be grouped to provide a third set ofcomponent monitoring indicators 226. In an embodiment, the third set ofcomponent monitoring indicators 226 are grouped to provide indicationsof a new, good, fair, or poor component. For a new indication, thegrouping is between the upper control limit 252 and plus two standarddeviation 254. In embodiments, for a good indication, the grouping isbetween plus two standard deviation 254 and minus two standard deviation262. For a fair indication, the grouping is between minus two standarddeviation 262 and the lower control limit 264. For a poor indication,the grouping is below the lower control limit 264. Grouping provides adifferent set of variations from the vehicle component data to bettergage the operational use and state of a vehicle component.

One example of determining at least one signal monitoring indicator fromthe vehicle component data will now be described. The signal monitoringindicators may include a first signal, a second signal, a third signaland a fourth signal. A first signal (“R”) when a single data point valuefrom the vehicle component data is below the lower control limit 264value is determined and identified. A second signal (“Y”) when a seriesof 8 consecutive data point values from the vehicle component data areon the same side of the mean value and either between the mean value andthe upper control limit 252 value or the mean value and the lowercontrol limit 262 is determined and identified. A third signal (“O”)when a series of 4 out of 5 consecutive data point values from thevehicle component data are either on the same side of the mean value andgreater than one sigma value or on the same side of the mean value andgreater than minus one sigma value is determined and identified. Afourth signal (“B”) when a series of 2 out of 3 consecutive data pointvalues from the vehicle component data are either between the mean valueand greater than the two sigma value or between the mean value andgreater than the minus two sigma value is determined and identified.

The next sequence in the process is component failure analysis.Component failure analysis uses the component event data and at leastone of the determined mean, determined moving average, determined movingrange, the first set of component indicators, the second set ofcomponent indicators, the third set of component indicators and/or atleast one signal monitoring indicator. In embodiments, the analysiscompares the determined data values from the component data before thecomponent event, or after the component event, or before and after thecomponent event. The analysis determines one of a component failure, apremature replacement, inactive failure maintenance or an erroneouscomponent event. The analysis also determines a set of criteria fordetermining one of a component failure, a premature replacement,inactive failure maintenance or an erroneous component event. Thecriteria are in the form of a plurality of indicators such as a failureindicator, predictive maintenance indicator and a maintenance validationindicator.

Referring now to FIG. 17, FIG. 19 and FIG. 27, the component failuredetermination logic is described. The logic is generally indicated at600. For this case, the vehicle event data point includes a date and aknown vehicle component event of failure.

For the vehicle component data preceding the vehicle event data point,if the moving average data value is not below the mean and notdecreasing over time, indicate this is not a vehicle component failure.If the moving average data value is below the mean and decreasing overtime, continue to check the signal monitoring indicators.

For the vehicle component data preceding the vehicle event data point,if the value of the signal monitoring indicators is not below the meanand not decreasing, indicate this is not a vehicle component failure. Ifthe value of the signal monitoring indicators is below the mean anddecreasing, check the moving average data point.

For the vehicle component data following the vehicle event data point,if the moving average data is not increasing over time, indicate this isnot a vehicle component failure. If the moving average data value isincreasing over time, check the signal monitoring indicators.

For vehicle component data following the vehicle event data point, ifthe value of the signal monitoring indicators is not above the mean,indicate this is not a vehicle component failure. If the value of thesignal monitoring indicators is above the mean, indicate componentfailure.

The determined criteria to indicate a component failure is at least oneof a moving average below the mean and decreasing before the vehicleevent data point, signal monitoring indicators below the mean and abovethe upper control limit before the vehicle event data point, the fourthset (“R”) of signal monitoring indicators below the lower control limitand moving further away from the lower control limit preceding thevehicle event data point. The fourth set (“R”) of signal monitoringindicators below the moving average before the vehicle event data point.

Referring now to FIG. 17, FIG. 21 and FIG. 28, the premature componentreplacement determination logic is described. The logic is generallyindicated at 700. For this case, the vehicle event data point includes adate and a known vehicle component event of component replacement.

For the vehicle component data preceding the vehicle event data point,if the moving average data value is not relatively constant or is belowminus two standard deviation over time, indicate this is not a prematurecomponent replacement event. If the moving average data value isrelatively constant or above minus two standard deviation over time,then continue and check the signal monitoring indicators.

For the vehicle component data preceding the vehicle event data point,if the value of signal monitoring indicators is below the lower controllimit, indicate this is not a premature component replacement event. Ifthe value of the signal monitoring indicates is above the lower controllimit, then continue and check the moving average data value following avehicle event data point.

For the vehicle component data following the vehicle event data point,if the moving average data value is not increasing over time, indicatethis is not a premature component replacement event. If the movingaverage data value is increasing over time, then continue and check thesignal monitoring indicators.

For the vehicle component data following the vehicle event data point,if the value of signal monitoring signals is not above the mean,indicate this is not a premature component replacement event. If thevalue of signal monitoring signals is above the mean, indicate apremature component event.

The determined criteria to indicate a premature component replacementevent is at least one of a moving average above minus one standarddeviation or signal monitoring indicators above the upper control limitbefore the vehicle event data point.

Referring now to FIG. 17, FIG. 23 and FIG. 29, the inactive componentmaintenance determination logic is described. The logic is generallyindicated at 800. For this case, the vehicle event data point includes adate and a known vehicle component event of inactive maintenance.

For the vehicle component data preceding the vehicle event data point,if the moving average data value is not relatively constant or is belowminus two standard deviation over time, indicate this is not an inactivecomponent maintenance event. If the moving average data value isrelatively constant or above minus two standard deviation over time,then continue and check the signal monitoring indicators.

For the vehicle component data preceding the vehicle event data point,if the value of signal monitoring indicators is below the lower controllimit, indicate this is not an inactive component maintenance event. Ifthe value of the signal monitoring indicates is above the lower controllimit, then continue and check the moving average data value following avehicle event data point.

For the vehicle component data following the vehicle event data point,if the moving average data value is not increasing over time, indicatethis is not an inactive component maintenance event. If the movingaverage data value is increasing over time, then continue and check thesignal monitoring indicators.

For the vehicle component data following the vehicle event data point,if the value of signal monitoring signals is not above the mean,indicate this is not an inactive component maintenance event. If thevalue of signal monitoring signals is above the mean, indicate aninactive component maintenance event.

The determined criteria to indicate an inactive component maintenanceevent is at least one of a moving average above minus one standarddeviation or signal monitoring indicators above the upper control limitbefore the vehicle event data point.

Referring now to FIG. 17, FIG. 22 and FIG. 30, the erroneous event datedetermination logic is described. The logic is generally indicated at700. For this case, the vehicle event data point includes a date and aknown vehicle component event of component failure.

For the vehicle component data preceding the vehicle event data point,if the moving average data value is not relatively constant or is belowminus two standard deviation over time, indicate this is not anerroneous date event. If the moving average data value is relativelyconstant or above minus two standard deviation over time, then continueand check the signal monitoring indicators.

For the vehicle component data preceding the vehicle event data point,if the value of signal monitoring indicators is below the lower controllimit, indicates this is not an erroneous date event. If the value ofthe signal monitoring indicates is above the lower control limit, thencontinue and check the moving average data value following a vehicleevent data point.

For the vehicle component data following the vehicle event data point,if the moving average data value is increasing over time, indicate thisis not an erroneous date event. If the moving average data value is notincreasing over time, then continue and check the signal monitoringindicators.

For the vehicle component data following the vehicle event data point,if the value of signal monitoring signals is not above the mean,indicate this is not an erroneous date event. If the value of signalmonitoring signals is above the mean, indicate an erroneous date event.

The determined criteria to indicate an erroneous date event is at leastone of a moving average above minus one standard deviation or signalmonitoring indicators above the upper control limit before the vehicleevent data point.

Telematics Component Condition Monitoring Process

Referring now to FIG. 17, FIG. 23 and FIG. 31, the telematics componentcondition monitoring logic is described. The logic is generallyindicated at 1000. This process and logic may be implemented in a server19 or in a computing device 20 or in a vehicular telematics hardwaresystem 30 or a combination of a server, computing device and vehiculartelematics hardware system. This process may also be implemented as asystem including a vehicular telematics hardware system 30 and a remotedevice 44. Finally, this process may also be implemented as an apparatusthat includes a vehicular telematics hardware system 30.

The process begins by obtaining operational component data. In someembodiments the operational component data includes operational vehiclecomponent data, for example battery voltages and battery voltages basedupon a vehicle engine start from cranking an engine with a startermotor.

Predictive indicator parameters based upon historical operational lifecycle use are also obtained. In embodiments the historical operationallife cycle data include one or more of filtered data, a moving average,a running average, a mean, plus one standard deviation, plus twostandard deviation, an upper control limit, minus one standarddeviation, minus two standard deviation or a lower control limit. Thepredictive indicator parameters may also include signal monitoringindicators and values (“R” signal values, “Y” signal values, “B” signalvalues and “O” signal values. The predictive indicator parameters arepredetermined from the historical operational life cycle data.

A comparison is made between the operational component data and one ormore of the predictive indicator parameters checking for new componentstatus. A new component status is determined and based upon one or morepredictive indicator parameters alone or in combination. In anembodiment, a new component status is indicated when a moving average isincreasing from minus two standard deviation to a minus one standarddeviation. Alternatively, a new component status is indicated when themoving average is increasing from minus one standard deviation to amean. Alternatively, a new component status is indicated when a movingaverage in increasing from a mean to plus one standard deviation.Alternatively a new component status is indicated when signals arebetween a mean and an upper control limit or signals are between plustwo standard deviation and an upper control limit. The signals include amix of “B”, “Y” and “O” signal values.

Another comparison is made between the operational component data andone or more of the predictive indicator parameters checking for a goodcomponent status. A good component status is determined and based uponone or more predictive indicator parameters alone or in combination. Inan embodiment, a good component status is indicated when a movingaverage has a relatively constant slop between a mean and plus twostandard deviation. Alternatively, a good component status is indicatedwhen signals are present between a mean and plus two standard deviation.In an embodiment, the signals include a mix of “Y” and “O” signalvalues. In another embodiment, the signals further include “B” signalvalues. In an embodiment, there are more signals above the movingaverage and less signals below the moving average. In anotherembodiment, the number of signals above and below the moving average aresubstantially equal in number.

Another comparison is made between the operational component data andone or more of the predictive indicator parameters checking for a faircomponent status. A fair component status is also based upon one or morepredictive indicator parameters alone or in combination. In anembodiment, a fair component status is indicated when a moving averageis decreasing from a mean to minus one standard deviation.Alternatively, a fair component status is indicated when a movingaverage is decreasing from minus one standard deviation to minus twostandard deviation. Alternatively, a fair component status is indicatedwith signals between a mean and a lower control limit. The signals maybe one or more of “o”, “Y” and “B” signal values. Alternatively, thesignals may include “R” signal values at or below the lower controllimit.

Another comparison is made between the operational component data andone or more of the predictive indicator parameters checking for a poorcomponent status. A poor component status may include more than oneindication of poor. For example, a first level of poor indication may beprovided. This occurs when a moving average is decreasing between a meanand minus 1 standard deviation with signals below a lower control limit.In an embodiment, the signals are “R” signal values. A second level ofpoor indication may also be provided. This occurs when a moving averageis decreasing from minus one standard deviation to minus two standarddeviation with signals below a lower control limit. In an embodiment,the signals are “R” signal values. In addition, the fist level of poorindication and second level of poor indication may be combined toprovide an overall indication of poor.

If the moving average is below −1 SD and the signals are below the lowercontrol limit, indicate the component status as poor. In an embodiment,the signals include “R” signals below the lower control limit. Thenumber of “R” signals is increasing and the “R” signals are movingfurther away from the lower control limit. When the moving average is ator below −2 SD with “R” signals, the component status is very poor.Alternatively, the signals include “O” and “B” between the mean and thelower control limit.

In an embodiment, the predictive indicator parameters are based upon acollection of vehicle components. The predictive indicator parametersmay be separated by the different types of vehicle engine and differenttypes of fuel (gas, diesel, hybrid or electric. The predictive indicatorparameters may be further based upon vehicles with the same or similarvehicle characteristics.

For example, if the vehicle component is a battery, then a criteria fora battery replacement status could be the when the current minimumvoltage as represented by the operational component data of 8.5 voltsfrom a cranking event. Alternatively, another criteria could be when thevariation of the voltage is more than 0.85 volts from a cranking event.A battery rating of 10.70 may further be a criteria for a batteryreplacement indication. Each battery may receive a percentile rankingand if the percentile ranking is below 6.25% this may be a criteria fora battery replacement indication. The percentile ranking could furtherbe district based (local operational area) or nationally based (broadoperational area).

Telematics Optimal Component Replacement Indication Process

Referring now to FIG. 17, FIG. 23 and FIG. 32, the telematics componentcondition monitoring logic is described. The logic is generallyindicated at 1100. This process and logic may be implemented in a server19 or in a computing device 20 or in a vehicular telematics hardwaresystem 30 or a combination of a server, computing device and vehiculartelematics hardware system. This process may also be implemented as asystem including a vehicular telematics hardware system 30 and a remotedevice 44. Finally, this process may also be implemented as an apparatusthat includes a vehicular telematics hardware system 30.

This process begins by obtaining operational component data. Inembodiments the operational component data include operational vehiclecomponent data, for example battery voltages and battery voltages basedupon a vehicle engine start from cranking an engine with a startermotor.

Predictive indicator parameters based upon historical operational lifecycle use are obtained. In embodiments the historical operational lifecycle data includes one or more of filtered data, moving average,running average, mean, plus one standard deviation, plus two standarddeviation, an upper control limit, minus one standard deviation, minustwo standard deviation or a lower control limit. The predictiveindicator parameters are predetermined from the historical operationallife cycle data.

A comparison between a sample of operational component data is made withone or more predictive indicator parameters. The one or more predictiveindicator parameters in include a moving average, a mean, minus onestandard deviation, a lower control limit and signals. In embodiments,the comparison is made for a warning status and a replace status. Thewarning status provides a more time to replace the vehicle componentbefore the replace status. The replace status is close to the point ofvehicle component failure. A sample of operational component dataincludes data over a day of operation or a few days of operation of avehicle. Alternatively, a sample of operational component data includeseight separate data points of operational data. Alternatively, a sampleof operational component data includes at least eight separate datapoints of operational data.

A component replacement status may be determined and indicated as awarning status. In an embodiment, a warning status is determined if thesample of operational component data has a moving average decreasingfrom a mean to minus one standard deviation with signals between a meanand lower control limit and different signals at or below a lowercontrol limit. The signals between the mean and lower control limit areabove and below a moving average. They include “B” signal values and “Y”signal values. Additionally, signals at or below the lower control limitare “R” signal values.

A component replacement status may also be determined and indicated as areplacement status. In an embodiment, a replacement status is determinedif the sample of operational component data has a moving averagedecreasing from a minus one standard deviation to a minus two standarddeviation with signals between a mean and lower control limit anddifferent signals at or below a lower control limit. The signals betweenthe mean and lower control limit are above and below a moving average.They may include “B” signal values and “Y” signal values and “O” signalvalues. Additional signals at or below the lower control limit are “R”signal values.

Telematics Predictive Indicators of Vehicle Component Status

Referring now to FIG. 15, FIG. 16, FIG. 17, FIG. 35 and FIG. 36,determining and identifying predictive indicators of vehicle componentstatus is described. This process and logic may be implemented in aserver 19 or in a computing device 20 or in a vehicular telematicshardware system 30 or a combination of a server, computing device andvehicular telematics hardware system. This process may also beimplemented as a system including a vehicular telematics hardware system30 and a remote device 44. Finally, this process may also be implementedas an apparatus that includes a vehicular telematics hardware system 30.The determining process is illustrated at 1200 in FIG. 35 and theidentifying process is illustrated at 1210 in FIG. 35. Both processesmay be implemented as a method or as a system. In the case of a system,the system includes a telematics hardware device 30 and a remote device44. The telematics hardware device 30 monitors and logs operationalcomponent data. This data includes operational values from variousvehicle components. The operational component data also includes vehiclecomponent data based upon measured component events such as a crankingevent. The operational component data is communicated from thetelematics hardware device 30 to remote device 44. Over time, the logsof operational data provide an operational life cycle view of vehiclescomponents from new, good, fair, poor and failure.

In addition, management event data is also captured over time.Management data provides vehicle component records in the form ofcomponent or vehicle events. Vehicle component events may be a failureevent, a repair event or a replace event depending upon the correctiveaction of a management event.

The process begins by access or obtaining management event data. Then,operational vehicle component data is accessed or obtained prior to amanagement event data point and following a management event data point(prior and post). The operational vehicle component data is filtered.Filtering provides a moving average or a running average of theoperational vehicle component data. In addition, signals are derivedfrom the operational vehicle component data. The derived signals may beidentified between a lower control limit and an upper control limit,between a mean and upper control limit, between minus one standarddeviation and plus one standard deviation, or between minus two standdeviation and plus two standard deviation. In addition, the signals maybe above a moving average 218, on either side of a moving average 218,below a moving average 218 or moving from above a moving average 218 tobelow a moving average 218. The derived signals are representative of ameasured component event, for example a cranking event. A cranking eventis an example of an operational event that places a high operationalload on a vehicle component within the limits of the component. Thecranking event provides a series of battery voltages starting with theignition on voltage, a voltage representative of an active startermotor, a voltage after cranking where the battery is charging followedby a recovery voltage as energy is replaced into the battery followingthe cranking event. A lower cranking event voltage produces moresignals. The operational component data is associated with themanagement event data typically by database records.

A check for real time predictive indicators occurs to identify potentialreal time predictive indicators of operational vehicle component status.The check involves comparing the filtered operational component data andone or more signals prior to a vehicle component event data point. Thereis also a comparison of filtered operational component data with one ormore signals post a vehicle component event data point. The results ofthe comparison identify vehicle component status and associatedpredictive indicators of component status such as a new component, or agood component, or a fair component, or a poor component, or a replacedcomponent, or a failed component. The check also may involve alone or incombination comparing the filtered operational component data andsignals with an upper control limit 252, plus two standard deviation254, plus one standard deviation 256, a mean value 258, minus onestandard deviation 260, minus two standard deviation 262 and a lowercontrol limit 264. The check also may involve alone or in combinationcomparing the filtered operation component data within zonesrepresentative of vehicle component status. For example, a monitoringindicator framework may also be associated with the operationalcomponent data and the management event data. The monitoring indicatorframework includes different zones or areas representative of vehiclecomponent status. For example a first zone 240 representative of arelatively new component, a second zone 242 for a good component, athird zone 244 for a fair component, a fourth zone for poor component, afifth zone 248 for a failed component and a sixth zone 216 for a newcomponent.

The filtered operational component data and signals have a particular orunique pattern for the case of a new vehicle component status. This isillustrated within the sixth zone 250 that follows a failure event 210.There are a number of predictive indicators and parameters foroperational status that are identified by a check or examination fromwithin the sixth zone 250. Each predictive indicator may be alone or incombination with other predictive indicators. A moving average 218predictive indicator is identified to be initially increasing in valuefrom minus two standard deviation 262 towards plus one standarddeviation. Signal indicators are identified to be initially groupedbetween a mean 258 and an upper control limit and above the movingaverage 218. The signals include a mix of “Y” signal values, “O” signalvalues and “B” signal values. Then, the moving average 218 predictiveindicator is further identified to be relatively constant around a plus1 standard deviation 256 or the moving average is between a mean value258 and plus two standard deviation 254. At this point, the signalindicators are further identified to be grouped between a mean value 258and an upper control limit 252 on either side of the moving average 218.The signals continue to include a mix of “Y” signal values, “O” signalvalues and “B” signal values. Each of the identified predictiveindicators for a vehicle component status of new is recorded.

The filtered operational component data and signals also have aparticular or unique pattern for the case of a good vehicle componentstatus. This is illustrated within the second zone 242 that follows newvehicle component status in the sense of the vehicle component lifecycle. There are another number of predictive indicators and parametersfor operational status that are identified by a check or examinationfrom within the second zone 242. Again, each predictive indicator may bealone or in combination with other predictive indicators. A movingaverage 218 predictive indicator is identified between a mean value 258and an upper control limit 252. The moving average 218 is relativelyconstant between a mean value 258 and plus one standard deviation 256.Signal indicators are also identified and grouped between a mean value258 and an upper control limit 252. These signal indicators are alsopredominately above the moving average 218. These signal indicators area mix of “Y” signal values and “O” signal values. There may be a few “B”signal values as well. Each of the identified predictive indicators fora vehicle component status of good is recorded.

The filtered operational component data and signals have yet anotherparticular or unique pattern for the case of a fair vehicle componentstatus. This is illustrated within the third zone 244. There are anumber of predictive indicators and parameters for operational statusthat are identified by a check or examination from within the third zone244. Each predictive indicator in this zone may also be alone or incombination with other predictive indicators. A moving average 218predictive indicator identified to be relatively constant and locatedbetween plus one standard deviation 256 and minus one standard deviation260. There are signal predictive indicators grouped on either side of amean value 258. Above the moving average 218 the signals are a mix of“Y” signal values and “O” signal values. Below the moving average 218there are a mix of “Y” signal values, “O” signal values and “B” signalvalues. In addition, there may also be a grouping “R” signal valuesbelow a lower control limit 264. The “R” signal values are relativelyconstant in value and close to the lower control limit value. Each ofthe identified predictive indicators for a vehicle component status offair is recorded.

The filtered operational component data and signals have yet anotherparticular or unique pattern for the case of a poor vehicle componentstatus. This is illustrated within the fourth zone 246. There are anumber of predictive indicators and parameters for the operationalstatus that are identified by a check or examination from within thefourth zone 246. Again, each predictive indicator in this zone may alsobe alone or in combination with other predictive indicators. The movingaverage predictive indicator is decreasing in value from a mean value258 towards minus two standard deviation 262. The moving averagepredictive indicator may be further segmented into a range. The firstrange occurs when the moving average 218 is decreasing from a mean value258 to minus one standard deviation 260. A second range occurs when themoving average 218 further decreases from minus one standard deviation260 to minus two standard deviation 262. Signal indicators are alsoidentified to be between a mean value 258 and a lower control limit 264.The signals include a mix of “Y” signal values and “B” signal values oneither side of the moving average 218. There may also be a small numberof “O” signal values below the moving average 218 and further associatedwith the first range. In addition, there may also be “R” signal valuesbelow the lower control limit 264. Each of the identified predictiveindicators for a vehicle component status of fair is recorded.

The filtered operational component data and signals also have anotherparticular or unique pattern for the case of a replace vehicle componentstatus. This is illustrated within the fifth zone 248. Optionally, theindicators from the poor vehicle component status may be combined withthe replace vehicle component status to provide an additional level tomonitor and gauge replacement of a vehicle component. There are a numberof predictive indicators and parameters for operational status that areidentified by a check or examination from within the fifth zone 248.Again, each predictive indicator may be alone or in combination withother predictive indicators. The fifth zone 248 does not have a movingaverage indicator (located in the fourth zone 246). There is a groupingof signals below the lower control limit 264. The group of signals belowthe lower control limit 264 is moving further away from the lowercontrol limit 264. The signals are solely “R” signal values.

The indicators and parameters for a replace vehicle component status maybe further grouped to provide a better indication towards vehiclecomponent replacement when combining indicators and parameters for boththe fourth zone 246 and the fifth zone 248. A replacement warning may beindicated. This occurs when the moving average 218 is decreasing from amean value 258 to minus one standard deviation 260 with signals betweenthe mean value 258 and the lower control limit 264. The signals areabove and below the moving average 218 and include a mix of “O” signalvalues, “Y” signal values and “B” signal values. In addition, there aresignals below the lower control limit 264 and these signals are “R”signal values. A replacement warning may also be indicated. This occurswhen the moving average 218 is decreasing from minus one standarddeviation 260 towards minus two standard deviation 262. The signals arebetween the mean value 258 and the lower control limit and are above andbelow the moving average 218. These signals are “B” signal values and“Y” signal values. In addition, there are signals below the lowercontrol limit 264 and these signals are “R” signal values. The “R”signal values are moving further away in value from the lower controllimit 264. Each of the identified predictive indicators for this vehiclecomponent status is recorded.

Technical Effects

Embodiments described herein provide one or more technical effects andimprovements, for example, an ability to determine a set of monitoringindicators and signal monitoring indicators based upon a historicaltelematics record of component life cycle use data; an ability todetermine and derive monitoring indicator ranges and metrics and signalmonitoring values from component life cycle use data; an ability topredict component failure, premature component replacement, inactivecomponent maintenance and erroneous event dates; an ability to monitorthe condition of a component in real time; and/or an ability to providevehicle component replacement indications in real time in advance of acomponent failure event to optimize the useful life of a vehiclecomponent before failure.

It should be understood that aspects are described herein with referenceto certain illustrative embodiments. The illustrative embodimentsdescribed herein are not necessarily intended to show all aspects, butrather are used to describe a few illustrative embodiments. Thus,aspects described herein are not intended to be construed narrowly inview of the illustrative embodiments. In addition, it should beunderstood that certain features disclosed herein might be used alone orin any suitable combination with other features.

What is claimed is:
 1. A method of monitoring a battery of a vehicle,the method comprising: receiving a plurality of voltage signalsindicating a change in voltage of the battery at times associated with aplurality of crankings of a starter motor of the vehicle; determining,for each of the plurality of voltage signals, a maximum voltage and aminimum voltage of the voltage signal, to generate a plurality ofmaximum voltages and a plurality of minimum voltages for a time period;determining a variance in the plurality of minimum voltages for the timeperiod; generating a prediction of a state of the battery based at leastin part on the plurality of maximum voltages, the plurality of minimumvoltages, and the variance of the plurality of minimum voltages.
 2. Themethod of claim 1, wherein receiving the plurality of voltage signalscomprises receiving the plurality of voltage signals via a vehicletelematics system of the vehicle.
 3. The method of claim 1, whereingenerating the prediction of the state of the battery comprisesgenerating a prediction of whether and/or when the battery is likely tofail.
 4. The method of claim 1, wherein generating the prediction of thestate of the battery comprises generating a prediction of whether and/orwhen the battery is likely to fail based at least in part on anenvironmental condition.
 5. The method of claim 4, wherein theenvironmental condition is temperature.
 6. A system of monitoring abattery of a vehicle, the system comprising: a telematics hardwaredevice including a processor, memory, firmware and communicationscapability, a remote device including a processor, memory, software andcommunications capability, said telematics hardware device monitoring abattery of a vehicle and logging a plurality of voltage signals, saidtelematics hardware device communicating a log including a plurality ofvoltage signals, said remote device receiving a plurality of voltagesignals indicating a change in voltage of the battery at timesassociated with a plurality of crankings of a starter motor of thevehicle; said remote device determining, for each of the plurality ofvoltage signals, a maximum voltage and a minimum voltage of the voltagesignal, to generate a plurality of maximum voltages and a plurality ofminimum voltages for a time period; said remote device determining avariance in the plurality of minimum voltages for the time period; saidremote device generating a prediction of a state of the battery based atleast in part on the plurality of maximum voltages, the plurality ofminimum voltages, and the variance of the plurality of minimum voltages.7. The system of claim 6, wherein receiving the plurality of voltagesignals comprises receiving the plurality of voltage signals via avehicle telematics system of the vehicle.
 8. The system of claim 6,wherein generating the prediction of the state of the battery comprisesgenerating a prediction of whether and/or when the battery is likely tofail.
 9. The system of claim 6, wherein generating the prediction of thestate of the battery comprises generating a prediction of whether and/orwhen the battery is likely to fail based at least in part on anenvironmental condition.
 10. The system of claim 9, wherein theenvironmental condition is temperature.